Overview

Dataset statistics

Number of variables90
Number of observations38577
Missing cells2199282
Missing cells (%)63.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory27.8 MiB
Average record size in memory755.4 B

Variable types

Numeric10
Categorical23
Boolean2
Unsupported55

Alerts

pymnt_plan has constant value "False"Constant
initial_list_status has constant value "False"Constant
collections_12_mths_ex_med has constant value "0.0"Constant
policy_code has constant value "1"Constant
acc_now_delinq has constant value "0"Constant
chargeoff_within_12_mths has constant value "0.0"Constant
delinq_amnt has constant value "0"Constant
tax_liens has constant value "0.0"Constant
int_rate has a high cardinality: 370 distinct valuesHigh cardinality
emp_title has a high cardinality: 28027 distinct valuesHigh cardinality
issue_d has a high cardinality: 55 distinct valuesHigh cardinality
url has a high cardinality: 38577 distinct valuesHigh cardinality
desc has a high cardinality: 25803 distinct valuesHigh cardinality
title has a high cardinality: 19297 distinct valuesHigh cardinality
zip_code has a high cardinality: 822 distinct valuesHigh cardinality
id is highly overall correlated with member_id and 2 other fieldsHigh correlation
member_id is highly overall correlated with id and 2 other fieldsHigh correlation
loan_amnt is highly overall correlated with funded_amnt and 2 other fieldsHigh correlation
funded_amnt is highly overall correlated with loan_amnt and 2 other fieldsHigh correlation
funded_amnt_inv is highly overall correlated with loan_amnt and 2 other fieldsHigh correlation
installment is highly overall correlated with loan_amnt and 2 other fieldsHigh correlation
mths_since_last_delinq is highly overall correlated with mths_since_last_recordHigh correlation
mths_since_last_record is highly overall correlated with id and 3 other fieldsHigh correlation
grade is highly overall correlated with sub_gradeHigh correlation
sub_grade is highly overall correlated with gradeHigh correlation
issue_d is highly overall correlated with id and 1 other fieldsHigh correlation
pub_rec_bankruptcies is highly overall correlated with mths_since_last_recordHigh correlation
pub_rec_bankruptcies is highly imbalanced (83.7%)Imbalance
emp_title has 2386 (6.2%) missing valuesMissing
emp_length has 1033 (2.7%) missing valuesMissing
desc has 12527 (32.5%) missing valuesMissing
mths_since_last_delinq has 24905 (64.6%) missing valuesMissing
mths_since_last_record has 35837 (92.9%) missing valuesMissing
next_pymnt_d has 38577 (100.0%) missing valuesMissing
mths_since_last_major_derog has 38577 (100.0%) missing valuesMissing
annual_inc_joint has 38577 (100.0%) missing valuesMissing
dti_joint has 38577 (100.0%) missing valuesMissing
verification_status_joint has 38577 (100.0%) missing valuesMissing
tot_coll_amt has 38577 (100.0%) missing valuesMissing
tot_cur_bal has 38577 (100.0%) missing valuesMissing
open_acc_6m has 38577 (100.0%) missing valuesMissing
open_il_6m has 38577 (100.0%) missing valuesMissing
open_il_12m has 38577 (100.0%) missing valuesMissing
open_il_24m has 38577 (100.0%) missing valuesMissing
mths_since_rcnt_il has 38577 (100.0%) missing valuesMissing
total_bal_il has 38577 (100.0%) missing valuesMissing
il_util has 38577 (100.0%) missing valuesMissing
open_rv_12m has 38577 (100.0%) missing valuesMissing
open_rv_24m has 38577 (100.0%) missing valuesMissing
max_bal_bc has 38577 (100.0%) missing valuesMissing
all_util has 38577 (100.0%) missing valuesMissing
total_rev_hi_lim has 38577 (100.0%) missing valuesMissing
inq_fi has 38577 (100.0%) missing valuesMissing
total_cu_tl has 38577 (100.0%) missing valuesMissing
inq_last_12m has 38577 (100.0%) missing valuesMissing
acc_open_past_24mths has 38577 (100.0%) missing valuesMissing
avg_cur_bal has 38577 (100.0%) missing valuesMissing
bc_open_to_buy has 38577 (100.0%) missing valuesMissing
bc_util has 38577 (100.0%) missing valuesMissing
mo_sin_old_il_acct has 38577 (100.0%) missing valuesMissing
mo_sin_old_rev_tl_op has 38577 (100.0%) missing valuesMissing
mo_sin_rcnt_rev_tl_op has 38577 (100.0%) missing valuesMissing
mo_sin_rcnt_tl has 38577 (100.0%) missing valuesMissing
mort_acc has 38577 (100.0%) missing valuesMissing
mths_since_recent_bc has 38577 (100.0%) missing valuesMissing
mths_since_recent_bc_dlq has 38577 (100.0%) missing valuesMissing
mths_since_recent_inq has 38577 (100.0%) missing valuesMissing
mths_since_recent_revol_delinq has 38577 (100.0%) missing valuesMissing
num_accts_ever_120_pd has 38577 (100.0%) missing valuesMissing
num_actv_bc_tl has 38577 (100.0%) missing valuesMissing
num_actv_rev_tl has 38577 (100.0%) missing valuesMissing
num_bc_sats has 38577 (100.0%) missing valuesMissing
num_bc_tl has 38577 (100.0%) missing valuesMissing
num_il_tl has 38577 (100.0%) missing valuesMissing
num_op_rev_tl has 38577 (100.0%) missing valuesMissing
num_rev_accts has 38577 (100.0%) missing valuesMissing
num_rev_tl_bal_gt_0 has 38577 (100.0%) missing valuesMissing
num_sats has 38577 (100.0%) missing valuesMissing
num_tl_120dpd_2m has 38577 (100.0%) missing valuesMissing
num_tl_30dpd has 38577 (100.0%) missing valuesMissing
num_tl_90g_dpd_24m has 38577 (100.0%) missing valuesMissing
num_tl_op_past_12m has 38577 (100.0%) missing valuesMissing
pct_tl_nvr_dlq has 38577 (100.0%) missing valuesMissing
percent_bc_gt_75 has 38577 (100.0%) missing valuesMissing
pub_rec_bankruptcies has 697 (1.8%) missing valuesMissing
tot_hi_cred_lim has 38577 (100.0%) missing valuesMissing
total_bal_ex_mort has 38577 (100.0%) missing valuesMissing
total_bc_limit has 38577 (100.0%) missing valuesMissing
total_il_high_credit_limit has 38577 (100.0%) missing valuesMissing
annual_inc is highly skewed (γ1 = 31.19841374)Skewed
url is uniformly distributedUniform
id has unique valuesUnique
member_id has unique valuesUnique
url has unique valuesUnique
next_pymnt_d is an unsupported type, check if it needs cleaning or further analysisUnsupported
mths_since_last_major_derog is an unsupported type, check if it needs cleaning or further analysisUnsupported
annual_inc_joint is an unsupported type, check if it needs cleaning or further analysisUnsupported
dti_joint is an unsupported type, check if it needs cleaning or further analysisUnsupported
verification_status_joint is an unsupported type, check if it needs cleaning or further analysisUnsupported
tot_coll_amt is an unsupported type, check if it needs cleaning or further analysisUnsupported
tot_cur_bal is an unsupported type, check if it needs cleaning or further analysisUnsupported
open_acc_6m is an unsupported type, check if it needs cleaning or further analysisUnsupported
open_il_6m is an unsupported type, check if it needs cleaning or further analysisUnsupported
open_il_12m is an unsupported type, check if it needs cleaning or further analysisUnsupported
open_il_24m is an unsupported type, check if it needs cleaning or further analysisUnsupported
mths_since_rcnt_il is an unsupported type, check if it needs cleaning or further analysisUnsupported
total_bal_il is an unsupported type, check if it needs cleaning or further analysisUnsupported
il_util is an unsupported type, check if it needs cleaning or further analysisUnsupported
open_rv_12m is an unsupported type, check if it needs cleaning or further analysisUnsupported
open_rv_24m is an unsupported type, check if it needs cleaning or further analysisUnsupported
max_bal_bc is an unsupported type, check if it needs cleaning or further analysisUnsupported
all_util is an unsupported type, check if it needs cleaning or further analysisUnsupported
total_rev_hi_lim is an unsupported type, check if it needs cleaning or further analysisUnsupported
inq_fi is an unsupported type, check if it needs cleaning or further analysisUnsupported
total_cu_tl is an unsupported type, check if it needs cleaning or further analysisUnsupported
inq_last_12m is an unsupported type, check if it needs cleaning or further analysisUnsupported
acc_open_past_24mths is an unsupported type, check if it needs cleaning or further analysisUnsupported
avg_cur_bal is an unsupported type, check if it needs cleaning or further analysisUnsupported
bc_open_to_buy is an unsupported type, check if it needs cleaning or further analysisUnsupported
bc_util is an unsupported type, check if it needs cleaning or further analysisUnsupported
mo_sin_old_il_acct is an unsupported type, check if it needs cleaning or further analysisUnsupported
mo_sin_old_rev_tl_op is an unsupported type, check if it needs cleaning or further analysisUnsupported
mo_sin_rcnt_rev_tl_op is an unsupported type, check if it needs cleaning or further analysisUnsupported
mo_sin_rcnt_tl is an unsupported type, check if it needs cleaning or further analysisUnsupported
mort_acc is an unsupported type, check if it needs cleaning or further analysisUnsupported
mths_since_recent_bc is an unsupported type, check if it needs cleaning or further analysisUnsupported
mths_since_recent_bc_dlq is an unsupported type, check if it needs cleaning or further analysisUnsupported
mths_since_recent_inq is an unsupported type, check if it needs cleaning or further analysisUnsupported
mths_since_recent_revol_delinq is an unsupported type, check if it needs cleaning or further analysisUnsupported
num_accts_ever_120_pd is an unsupported type, check if it needs cleaning or further analysisUnsupported
num_actv_bc_tl is an unsupported type, check if it needs cleaning or further analysisUnsupported
num_actv_rev_tl is an unsupported type, check if it needs cleaning or further analysisUnsupported
num_bc_sats is an unsupported type, check if it needs cleaning or further analysisUnsupported
num_bc_tl is an unsupported type, check if it needs cleaning or further analysisUnsupported
num_il_tl is an unsupported type, check if it needs cleaning or further analysisUnsupported
num_op_rev_tl is an unsupported type, check if it needs cleaning or further analysisUnsupported
num_rev_accts is an unsupported type, check if it needs cleaning or further analysisUnsupported
num_rev_tl_bal_gt_0 is an unsupported type, check if it needs cleaning or further analysisUnsupported
num_sats is an unsupported type, check if it needs cleaning or further analysisUnsupported
num_tl_120dpd_2m is an unsupported type, check if it needs cleaning or further analysisUnsupported
num_tl_30dpd is an unsupported type, check if it needs cleaning or further analysisUnsupported
num_tl_90g_dpd_24m is an unsupported type, check if it needs cleaning or further analysisUnsupported
num_tl_op_past_12m is an unsupported type, check if it needs cleaning or further analysisUnsupported
pct_tl_nvr_dlq is an unsupported type, check if it needs cleaning or further analysisUnsupported
percent_bc_gt_75 is an unsupported type, check if it needs cleaning or further analysisUnsupported
tot_hi_cred_lim is an unsupported type, check if it needs cleaning or further analysisUnsupported
total_bal_ex_mort is an unsupported type, check if it needs cleaning or further analysisUnsupported
total_bc_limit is an unsupported type, check if it needs cleaning or further analysisUnsupported
total_il_high_credit_limit is an unsupported type, check if it needs cleaning or further analysisUnsupported
mths_since_last_delinq has 443 (1.1%) zerosZeros
mths_since_last_record has 670 (1.7%) zerosZeros

Reproduction

Analysis started2023-02-05 14:17:04.545371
Analysis finished2023-02-05 14:17:23.469841
Duration18.92 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct38577
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean676378.71
Minimum54734
Maximum1077501
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size602.8 KiB
2023-02-05T19:47:23.544659image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum54734
5-th percentile371001.4
Q1512033
median656423
Q3829146
95-th percentile1038337
Maximum1077501
Range1022767
Interquartile range (IQR)317113

Descriptive statistics

Standard deviation209263.9
Coefficient of variation (CV)0.30938866
Kurtosis-0.6868733
Mean676378.71
Median Absolute Deviation (MAD)156121
Skewness0.11702212
Sum2.6092661 × 1010
Variance4.3791379 × 1010
MonotonicityNot monotonic
2023-02-05T19:47:23.664923image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1077501 1
 
< 0.1%
562133 1
 
< 0.1%
558545 1
 
< 0.1%
562256 1
 
< 0.1%
562224 1
 
< 0.1%
561407 1
 
< 0.1%
562178 1
 
< 0.1%
562173 1
 
< 0.1%
560051 1
 
< 0.1%
561718 1
 
< 0.1%
Other values (38567) 38567
> 99.9%
ValueCountFrequency (%)
54734 1
< 0.1%
55742 1
< 0.1%
57245 1
< 0.1%
57416 1
< 0.1%
58915 1
< 0.1%
59006 1
< 0.1%
61390 1
< 0.1%
61419 1
< 0.1%
62102 1
< 0.1%
65426 1
< 0.1%
ValueCountFrequency (%)
1077501 1
< 0.1%
1077430 1
< 0.1%
1077175 1
< 0.1%
1076863 1
< 0.1%
1075269 1
< 0.1%
1072053 1
< 0.1%
1071795 1
< 0.1%
1071570 1
< 0.1%
1070078 1
< 0.1%
1069971 1
< 0.1%

member_id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct38577
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean842284.34
Minimum70699
Maximum1314167
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size602.8 KiB
2023-02-05T19:47:23.763764image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum70699
5-th percentile385845.2
Q1661131
median839292
Q31037336
95-th percentile1268022.2
Maximum1314167
Range1243468
Interquartile range (IQR)376205

Descriptive statistics

Standard deviation264451.93
Coefficient of variation (CV)0.3139699
Kurtosis-0.54673126
Mean842284.34
Median Absolute Deviation (MAD)189018
Skewness-0.18067854
Sum3.2492803 × 1010
Variance6.9934823 × 1010
MonotonicityNot monotonic
2023-02-05T19:47:23.871692image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1296599 1
 
< 0.1%
723395 1
 
< 0.1%
719001 1
 
< 0.1%
723527 1
 
< 0.1%
723494 1
 
< 0.1%
722509 1
 
< 0.1%
723441 1
 
< 0.1%
723436 1
 
< 0.1%
720879 1
 
< 0.1%
722882 1
 
< 0.1%
Other values (38567) 38567
> 99.9%
ValueCountFrequency (%)
70699 1
< 0.1%
73673 1
< 0.1%
74724 1
< 0.1%
76583 1
< 0.1%
80353 1
< 0.1%
80364 1
< 0.1%
84914 1
< 0.1%
85483 1
< 0.1%
86999 1
< 0.1%
89243 1
< 0.1%
ValueCountFrequency (%)
1314167 1
< 0.1%
1313524 1
< 0.1%
1311441 1
< 0.1%
1306957 1
< 0.1%
1306721 1
< 0.1%
1305201 1
< 0.1%
1305008 1
< 0.1%
1304956 1
< 0.1%
1304884 1
< 0.1%
1304871 1
< 0.1%

loan_amnt
Real number (ℝ)

Distinct870
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11047.025
Minimum500
Maximum35000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size602.8 KiB
2023-02-05T19:47:23.977644image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile2400
Q15300
median9600
Q315000
95-th percentile25000
Maximum35000
Range34500
Interquartile range (IQR)9700

Descriptive statistics

Standard deviation7348.4416
Coefficient of variation (CV)0.66519641
Kurtosis0.84295245
Mean11047.025
Median Absolute Deviation (MAD)4600
Skewness1.0781027
Sum4.261611 × 108
Variance53999595
MonotonicityNot monotonic
2023-02-05T19:47:24.077252image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 2809
 
7.3%
12000 2248
 
5.8%
5000 2028
 
5.3%
6000 1886
 
4.9%
15000 1838
 
4.8%
8000 1568
 
4.1%
20000 1536
 
4.0%
25000 1327
 
3.4%
4000 1123
 
2.9%
3000 1018
 
2.6%
Other values (860) 21196
54.9%
ValueCountFrequency (%)
500 5
 
< 0.1%
700 1
 
< 0.1%
725 1
 
< 0.1%
750 1
 
< 0.1%
800 1
 
< 0.1%
900 2
 
< 0.1%
950 1
 
< 0.1%
1000 298
0.8%
1050 4
 
< 0.1%
1075 1
 
< 0.1%
ValueCountFrequency (%)
35000 601
1.6%
34800 2
 
< 0.1%
34675 1
 
< 0.1%
34525 1
 
< 0.1%
34475 5
 
< 0.1%
34200 1
 
< 0.1%
34000 13
 
< 0.1%
33950 8
 
< 0.1%
33600 4
 
< 0.1%
33500 2
 
< 0.1%

funded_amnt
Real number (ℝ)

Distinct1019
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10784.059
Minimum500
Maximum35000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size602.8 KiB
2023-02-05T19:47:24.183623image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile2400
Q15200
median9550
Q315000
95-th percentile25000
Maximum35000
Range34500
Interquartile range (IQR)9800

Descriptive statistics

Standard deviation7090.306
Coefficient of variation (CV)0.6574803
Kurtosis1.0252786
Mean10784.059
Median Absolute Deviation (MAD)4550
Skewness1.1038284
Sum4.1601662 × 108
Variance50272440
MonotonicityNot monotonic
2023-02-05T19:47:24.285251image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 2719
 
7.0%
12000 2161
 
5.6%
5000 2017
 
5.2%
6000 1876
 
4.9%
15000 1732
 
4.5%
8000 1556
 
4.0%
20000 1368
 
3.5%
4000 1120
 
2.9%
25000 1080
 
2.8%
3000 1010
 
2.6%
Other values (1009) 21938
56.9%
ValueCountFrequency (%)
500 5
 
< 0.1%
700 1
 
< 0.1%
725 1
 
< 0.1%
750 1
 
< 0.1%
800 1
 
< 0.1%
900 2
 
< 0.1%
950 1
 
< 0.1%
1000 299
0.8%
1050 5
 
< 0.1%
1075 1
 
< 0.1%
ValueCountFrequency (%)
35000 499
1.3%
34800 1
 
< 0.1%
34675 2
 
< 0.1%
34525 1
 
< 0.1%
34475 4
 
< 0.1%
34250 1
 
< 0.1%
34000 12
 
< 0.1%
33950 6
 
< 0.1%
33600 4
 
< 0.1%
33500 1
 
< 0.1%

funded_amnt_inv
Real number (ℝ)

Distinct8050
Distinct (%)20.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10222.481
Minimum0
Maximum35000
Zeros129
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size602.8 KiB
2023-02-05T19:47:24.391365image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1800
Q15000
median8733.44
Q314000
95-th percentile24500.067
Maximum35000
Range35000
Interquartile range (IQR)9000

Descriptive statistics

Standard deviation7022.7206
Coefficient of variation (CV)0.68698788
Kurtosis1.1648002
Mean10222.481
Median Absolute Deviation (MAD)4066.56
Skewness1.1299968
Sum3.9435265 × 108
Variance49318605
MonotonicityNot monotonic
2023-02-05T19:47:24.488684image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 1294
 
3.4%
10000 1264
 
3.3%
6000 1182
 
3.1%
12000 1016
 
2.6%
8000 887
 
2.3%
4000 806
 
2.1%
3000 790
 
2.0%
15000 630
 
1.6%
7000 596
 
1.5%
2000 448
 
1.2%
Other values (8040) 29664
76.9%
ValueCountFrequency (%)
0 129
0.3%
0.000121098 1
 
< 0.1%
0.000531133 1
 
< 0.1%
0.000654607 1
 
< 0.1%
0.001867696 1
 
< 0.1%
0.001963093 1
 
< 0.1%
0.001966974 1
 
< 0.1%
0.002251738 1
 
< 0.1%
0.002283598 1
 
< 0.1%
0.002373058 1
 
< 0.1%
ValueCountFrequency (%)
35000 127
0.3%
34997.35245 1
 
< 0.1%
34993.65539 1
 
< 0.1%
34993.32571 1
 
< 0.1%
34993.26306 1
 
< 0.1%
34993.19696 1
 
< 0.1%
34990.4308 1
 
< 0.1%
34987.27101 1
 
< 0.1%
34977.34674 1
 
< 0.1%
34975.81636 1
 
< 0.1%

term
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size602.8 KiB
36 months
29096 
60 months
9481 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters385770
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row 36 months
2nd row 60 months
3rd row 36 months
4th row 36 months
5th row 36 months

Common Values

ValueCountFrequency (%)
36 months 29096
75.4%
60 months 9481
 
24.6%

Length

2023-02-05T19:47:24.580124image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-05T19:47:24.667476image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
months 38577
50.0%
36 29096
37.7%
60 9481
 
12.3%

Most occurring characters

ValueCountFrequency (%)
77154
20.0%
6 38577
10.0%
m 38577
10.0%
o 38577
10.0%
n 38577
10.0%
t 38577
10.0%
h 38577
10.0%
s 38577
10.0%
3 29096
 
7.5%
0 9481
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 231462
60.0%
Space Separator 77154
 
20.0%
Decimal Number 77154
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m 38577
16.7%
o 38577
16.7%
n 38577
16.7%
t 38577
16.7%
h 38577
16.7%
s 38577
16.7%
Decimal Number
ValueCountFrequency (%)
6 38577
50.0%
3 29096
37.7%
0 9481
 
12.3%
Space Separator
ValueCountFrequency (%)
77154
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 231462
60.0%
Common 154308
40.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
m 38577
16.7%
o 38577
16.7%
n 38577
16.7%
t 38577
16.7%
h 38577
16.7%
s 38577
16.7%
Common
ValueCountFrequency (%)
77154
50.0%
6 38577
25.0%
3 29096
 
18.9%
0 9481
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 385770
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
77154
20.0%
6 38577
10.0%
m 38577
10.0%
o 38577
10.0%
n 38577
10.0%
t 38577
10.0%
h 38577
10.0%
s 38577
10.0%
3 29096
 
7.5%
0 9481
 
2.5%

int_rate
Categorical

Distinct370
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size602.8 KiB
10.99%
 
913
11.49%
 
790
7.51%
 
787
13.49%
 
749
7.88%
 
725
Other values (365)
34613 

Length

Max length6
Median length6
Mean length5.687197
Min length5

Characters and Unicode

Total characters219395
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)< 0.1%

Sample

1st row10.65%
2nd row15.27%
3rd row15.96%
4th row13.49%
5th row7.90%

Common Values

ValueCountFrequency (%)
10.99% 913
 
2.4%
11.49% 790
 
2.0%
7.51% 787
 
2.0%
13.49% 749
 
1.9%
7.88% 725
 
1.9%
7.49% 651
 
1.7%
9.99% 590
 
1.5%
7.90% 574
 
1.5%
5.42% 573
 
1.5%
11.71% 559
 
1.4%
Other values (360) 31666
82.1%

Length

2023-02-05T19:47:24.739134image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10.99 913
 
2.4%
11.49 790
 
2.0%
7.51 787
 
2.0%
13.49 749
 
1.9%
7.88 725
 
1.9%
7.49 651
 
1.7%
9.99 590
 
1.5%
7.90 574
 
1.5%
5.42 573
 
1.5%
11.71 559
 
1.4%
Other values (360) 31666
82.1%

Most occurring characters

ValueCountFrequency (%)
. 38577
17.6%
% 38577
17.6%
1 36981
16.9%
9 20915
9.5%
2 12253
 
5.6%
7 11763
 
5.4%
6 11763
 
5.4%
4 10740
 
4.9%
3 9749
 
4.4%
5 9723
 
4.4%
Other values (2) 18354
8.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 142241
64.8%
Other Punctuation 77154
35.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 36981
26.0%
9 20915
14.7%
2 12253
 
8.6%
7 11763
 
8.3%
6 11763
 
8.3%
4 10740
 
7.6%
3 9749
 
6.9%
5 9723
 
6.8%
8 9341
 
6.6%
0 9013
 
6.3%
Other Punctuation
ValueCountFrequency (%)
. 38577
50.0%
% 38577
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 219395
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 38577
17.6%
% 38577
17.6%
1 36981
16.9%
9 20915
9.5%
2 12253
 
5.6%
7 11763
 
5.4%
6 11763
 
5.4%
4 10740
 
4.9%
3 9749
 
4.4%
5 9723
 
4.4%
Other values (2) 18354
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 219395
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 38577
17.6%
% 38577
17.6%
1 36981
16.9%
9 20915
9.5%
2 12253
 
5.6%
7 11763
 
5.4%
6 11763
 
5.4%
4 10740
 
4.9%
3 9749
 
4.4%
5 9723
 
4.4%
Other values (2) 18354
8.4%

installment
Real number (ℝ)

Distinct15022
Distinct (%)38.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean322.46632
Minimum15.69
Maximum1305.19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size602.8 KiB
2023-02-05T19:47:24.825152image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum15.69
5-th percentile70.61
Q1165.74
median277.86
Q3425.55
95-th percentile760.82
Maximum1305.19
Range1289.5
Interquartile range (IQR)259.81

Descriptive statistics

Standard deviation208.63921
Coefficient of variation (CV)0.64701087
Kurtosis1.3179975
Mean322.46632
Median Absolute Deviation (MAD)121.76
Skewness1.1504865
Sum12439783
Variance43530.322
MonotonicityNot monotonic
2023-02-05T19:47:24.928965image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
311.11 68
 
0.2%
180.96 59
 
0.2%
311.02 54
 
0.1%
150.8 48
 
0.1%
368.45 46
 
0.1%
372.12 45
 
0.1%
330.76 43
 
0.1%
339.31 42
 
0.1%
317.72 41
 
0.1%
301.6 41
 
0.1%
Other values (15012) 38090
98.7%
ValueCountFrequency (%)
15.69 1
< 0.1%
16.08 1
< 0.1%
16.25 1
< 0.1%
16.31 1
< 0.1%
16.47 1
< 0.1%
19.87 1
< 0.1%
20.22 1
< 0.1%
21.25 1
< 0.1%
21.81 1
< 0.1%
22.51 1
< 0.1%
ValueCountFrequency (%)
1305.19 1
 
< 0.1%
1302.69 1
 
< 0.1%
1295.21 1
 
< 0.1%
1288.1 2
 
< 0.1%
1283.5 1
 
< 0.1%
1276.6 3
< 0.1%
1272.2 1
 
< 0.1%
1269.73 5
< 0.1%
1265.16 1
 
< 0.1%
1263.23 1
 
< 0.1%

grade
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size602.8 KiB
B
11675 
A
10045 
C
7834 
D
5085 
E
2663 
Other values (2)
1275 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters38577
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowC
3rd rowC
4th rowC
5th rowA

Common Values

ValueCountFrequency (%)
B 11675
30.3%
A 10045
26.0%
C 7834
20.3%
D 5085
13.2%
E 2663
 
6.9%
F 976
 
2.5%
G 299
 
0.8%

Length

2023-02-05T19:47:25.016844image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-05T19:47:25.107210image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
b 11675
30.3%
a 10045
26.0%
c 7834
20.3%
d 5085
13.2%
e 2663
 
6.9%
f 976
 
2.5%
g 299
 
0.8%

Most occurring characters

ValueCountFrequency (%)
B 11675
30.3%
A 10045
26.0%
C 7834
20.3%
D 5085
13.2%
E 2663
 
6.9%
F 976
 
2.5%
G 299
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 38577
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 11675
30.3%
A 10045
26.0%
C 7834
20.3%
D 5085
13.2%
E 2663
 
6.9%
F 976
 
2.5%
G 299
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 38577
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 11675
30.3%
A 10045
26.0%
C 7834
20.3%
D 5085
13.2%
E 2663
 
6.9%
F 976
 
2.5%
G 299
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38577
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 11675
30.3%
A 10045
26.0%
C 7834
20.3%
D 5085
13.2%
E 2663
 
6.9%
F 976
 
2.5%
G 299
 
0.8%

sub_grade
Categorical

Distinct35
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size602.8 KiB
A4
2873 
B3
2825 
A5
2715 
B5
2615 
B4
 
2437
Other values (30)
25112 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters77154
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB2
2nd rowC4
3rd rowC5
4th rowC1
5th rowA4

Common Values

ValueCountFrequency (%)
A4 2873
 
7.4%
B3 2825
 
7.3%
A5 2715
 
7.0%
B5 2615
 
6.8%
B4 2437
 
6.3%
C1 2055
 
5.3%
B2 2001
 
5.2%
C2 1931
 
5.0%
A3 1810
 
4.7%
B1 1797
 
4.7%
Other values (25) 15518
40.2%

Length

2023-02-05T19:47:25.193334image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a4 2873
 
7.4%
b3 2825
 
7.3%
a5 2715
 
7.0%
b5 2615
 
6.8%
b4 2437
 
6.3%
c1 2055
 
5.3%
b2 2001
 
5.2%
c2 1931
 
5.0%
a3 1810
 
4.7%
b1 1797
 
4.7%
Other values (25) 15518
40.2%

Most occurring characters

ValueCountFrequency (%)
B 11675
15.1%
A 10045
13.0%
4 8063
10.5%
3 7974
10.3%
5 7847
10.2%
C 7834
10.2%
2 7650
9.9%
1 7043
9.1%
D 5085
6.6%
E 2663
 
3.5%
Other values (2) 1275
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 38577
50.0%
Decimal Number 38577
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 11675
30.3%
A 10045
26.0%
C 7834
20.3%
D 5085
13.2%
E 2663
 
6.9%
F 976
 
2.5%
G 299
 
0.8%
Decimal Number
ValueCountFrequency (%)
4 8063
20.9%
3 7974
20.7%
5 7847
20.3%
2 7650
19.8%
1 7043
18.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 38577
50.0%
Common 38577
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 11675
30.3%
A 10045
26.0%
C 7834
20.3%
D 5085
13.2%
E 2663
 
6.9%
F 976
 
2.5%
G 299
 
0.8%
Common
ValueCountFrequency (%)
4 8063
20.9%
3 7974
20.7%
5 7847
20.3%
2 7650
19.8%
1 7043
18.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 77154
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 11675
15.1%
A 10045
13.0%
4 8063
10.5%
3 7974
10.3%
5 7847
10.2%
C 7834
10.2%
2 7650
9.9%
1 7043
9.1%
D 5085
6.6%
E 2663
 
3.5%
Other values (2) 1275
 
1.7%

emp_title
Categorical

HIGH CARDINALITY  MISSING 

Distinct28027
Distinct (%)77.4%
Missing2386
Missing (%)6.2%
Memory size602.8 KiB
US Army
 
131
Bank of America
 
107
IBM
 
65
AT&T
 
57
Kaiser Permanente
 
56
Other values (28022)
35775 

Length

Max length78
Median length55
Mean length18.34959
Min length2

Characters and Unicode

Total characters664090
Distinct characters96
Distinct categories15 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24937 ?
Unique (%)68.9%

Sample

1st rowRyder
2nd rowAIR RESOURCES BOARD
3rd rowVeolia Transportaton
4th rowSouthern Star Photography
5th rowMKC Accounting

Common Values

ValueCountFrequency (%)
US Army 131
 
0.3%
Bank of America 107
 
0.3%
IBM 65
 
0.2%
AT&T 57
 
0.1%
Kaiser Permanente 56
 
0.1%
Wells Fargo 52
 
0.1%
USAF 52
 
0.1%
UPS 52
 
0.1%
US Air Force 51
 
0.1%
Walmart 45
 
0.1%
Other values (28017) 35523
92.1%
(Missing) 2386
 
6.2%

Length

2023-02-05T19:47:25.297234image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
inc 3111
 
3.2%
of 2913
 
3.0%
1175
 
1.2%
and 931
 
1.0%
center 794
 
0.8%
bank 789
 
0.8%
county 770
 
0.8%
services 770
 
0.8%
the 728
 
0.7%
school 726
 
0.7%
Other values (18510) 84847
87.0%

Most occurring characters

ValueCountFrequency (%)
62750
 
9.4%
e 54249
 
8.2%
a 42526
 
6.4%
n 41351
 
6.2%
o 41275
 
6.2%
i 39292
 
5.9%
r 38852
 
5.9%
t 37392
 
5.6%
s 29372
 
4.4%
l 25136
 
3.8%
Other values (86) 251895
37.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 474455
71.4%
Uppercase Letter 116049
 
17.5%
Space Separator 62750
 
9.4%
Other Punctuation 8564
 
1.3%
Dash Punctuation 1008
 
0.2%
Decimal Number 929
 
0.1%
Open Punctuation 152
 
< 0.1%
Close Punctuation 150
 
< 0.1%
Math Symbol 21
 
< 0.1%
Other Number 2
 
< 0.1%
Other values (5) 10
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 14131
 
12.2%
S 12931
 
11.1%
A 8624
 
7.4%
I 7357
 
6.3%
M 6336
 
5.5%
P 5923
 
5.1%
T 5522
 
4.8%
L 5385
 
4.6%
E 5083
 
4.4%
D 4905
 
4.2%
Other values (18) 39852
34.3%
Lowercase Letter
ValueCountFrequency (%)
e 54249
11.4%
a 42526
9.0%
n 41351
8.7%
o 41275
8.7%
i 39292
 
8.3%
r 38852
 
8.2%
t 37392
 
7.9%
s 29372
 
6.2%
l 25136
 
5.3%
c 22413
 
4.7%
Other values (17) 102597
21.6%
Other Punctuation
ValueCountFrequency (%)
. 4148
48.4%
, 2137
25.0%
& 1265
 
14.8%
' 630
 
7.4%
/ 300
 
3.5%
# 34
 
0.4%
@ 10
 
0.1%
: 9
 
0.1%
! 8
 
0.1%
" 8
 
0.1%
Other values (5) 15
 
0.2%
Decimal Number
ValueCountFrequency (%)
1 183
19.7%
2 155
16.7%
3 152
16.4%
0 93
10.0%
4 86
9.3%
5 69
 
7.4%
9 61
 
6.6%
6 57
 
6.1%
7 42
 
4.5%
8 31
 
3.3%
Math Symbol
ValueCountFrequency (%)
+ 18
85.7%
| 2
 
9.5%
< 1
 
4.8%
Open Punctuation
ValueCountFrequency (%)
( 151
99.3%
[ 1
 
0.7%
Currency Symbol
ValueCountFrequency (%)
¢ 1
50.0%
$ 1
50.0%
Control
ValueCountFrequency (%)
€ 1
50.0%
ƒ 1
50.0%
Space Separator
ValueCountFrequency (%)
62750
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1008
100.0%
Close Punctuation
ValueCountFrequency (%)
) 150
100.0%
Other Number
ValueCountFrequency (%)
² 2
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 2
100.0%
Other Symbol
ValueCountFrequency (%)
© 2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 590504
88.9%
Common 73586
 
11.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 54249
 
9.2%
a 42526
 
7.2%
n 41351
 
7.0%
o 41275
 
7.0%
i 39292
 
6.7%
r 38852
 
6.6%
t 37392
 
6.3%
s 29372
 
5.0%
l 25136
 
4.3%
c 22413
 
3.8%
Other values (45) 218646
37.0%
Common
ValueCountFrequency (%)
62750
85.3%
. 4148
 
5.6%
, 2137
 
2.9%
& 1265
 
1.7%
- 1008
 
1.4%
' 630
 
0.9%
/ 300
 
0.4%
1 183
 
0.2%
2 155
 
0.2%
3 152
 
0.2%
Other values (31) 858
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 664076
> 99.9%
None 14
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
62750
 
9.4%
e 54249
 
8.2%
a 42526
 
6.4%
n 41351
 
6.2%
o 41275
 
6.2%
i 39292
 
5.9%
r 38852
 
5.9%
t 37392
 
5.6%
s 29372
 
4.4%
l 25136
 
3.8%
Other values (77) 251881
37.9%
None
ValueCountFrequency (%)
à 3
21.4%
² 2
14.3%
© 2
14.3%
 2
14.3%
¢ 1
 
7.1%
€ 1
 
7.1%
â 1
 
7.1%
ƒ 1
 
7.1%
¡ 1
 
7.1%

emp_length
Categorical

Distinct11
Distinct (%)< 0.1%
Missing1033
Missing (%)2.7%
Memory size602.8 KiB
10+ years
8488 
< 1 year
4508 
2 years
4291 
3 years
4012 
4 years
3342 
Other values (6)
12903 

Length

Max length9
Median length7
Mean length7.4878276
Min length6

Characters and Unicode

Total characters281123
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10+ years
2nd row< 1 year
3rd row10+ years
4th row10+ years
5th row3 years

Common Values

ValueCountFrequency (%)
10+ years 8488
22.0%
< 1 year 4508
11.7%
2 years 4291
11.1%
3 years 4012
10.4%
4 years 3342
 
8.7%
5 years 3194
 
8.3%
1 year 3169
 
8.2%
6 years 2168
 
5.6%
7 years 1711
 
4.4%
8 years 1435
 
3.7%

Length

2023-02-05T19:47:25.401312image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
years 29867
37.5%
10 8488
 
10.7%
1 7677
 
9.6%
year 7677
 
9.6%
4508
 
5.7%
2 4291
 
5.4%
3 4012
 
5.0%
4 3342
 
4.2%
5 3194
 
4.0%
6 2168
 
2.7%
Other values (3) 4372
 
5.5%

Most occurring characters

ValueCountFrequency (%)
42052
15.0%
y 37544
13.4%
e 37544
13.4%
a 37544
13.4%
r 37544
13.4%
s 29867
10.6%
1 16165
 
5.8%
0 8488
 
3.0%
+ 8488
 
3.0%
< 4508
 
1.6%
Other values (8) 21379
7.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 180043
64.0%
Decimal Number 46032
 
16.4%
Space Separator 42052
 
15.0%
Math Symbol 12996
 
4.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 16165
35.1%
0 8488
18.4%
2 4291
 
9.3%
3 4012
 
8.7%
4 3342
 
7.3%
5 3194
 
6.9%
6 2168
 
4.7%
7 1711
 
3.7%
8 1435
 
3.1%
9 1226
 
2.7%
Lowercase Letter
ValueCountFrequency (%)
y 37544
20.9%
e 37544
20.9%
a 37544
20.9%
r 37544
20.9%
s 29867
16.6%
Math Symbol
ValueCountFrequency (%)
+ 8488
65.3%
< 4508
34.7%
Space Separator
ValueCountFrequency (%)
42052
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 180043
64.0%
Common 101080
36.0%

Most frequent character per script

Common
ValueCountFrequency (%)
42052
41.6%
1 16165
 
16.0%
0 8488
 
8.4%
+ 8488
 
8.4%
< 4508
 
4.5%
2 4291
 
4.2%
3 4012
 
4.0%
4 3342
 
3.3%
5 3194
 
3.2%
6 2168
 
2.1%
Other values (3) 4372
 
4.3%
Latin
ValueCountFrequency (%)
y 37544
20.9%
e 37544
20.9%
a 37544
20.9%
r 37544
20.9%
s 29867
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 281123
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
42052
15.0%
y 37544
13.4%
e 37544
13.4%
a 37544
13.4%
r 37544
13.4%
s 29867
10.6%
1 16165
 
5.8%
0 8488
 
3.0%
+ 8488
 
3.0%
< 4508
 
1.6%
Other values (8) 21379
7.6%

home_ownership
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size602.8 KiB
RENT
18480 
MORTGAGE
17021 
OWN
2975 
OTHER
 
98
NONE
 
3

Length

Max length8
Median length5
Mean length5.6903077
Min length3

Characters and Unicode

Total characters219515
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRENT
2nd rowRENT
3rd rowRENT
4th rowRENT
5th rowRENT

Common Values

ValueCountFrequency (%)
RENT 18480
47.9%
MORTGAGE 17021
44.1%
OWN 2975
 
7.7%
OTHER 98
 
0.3%
NONE 3
 
< 0.1%

Length

2023-02-05T19:47:25.481294image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-05T19:47:25.572930image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
rent 18480
47.9%
mortgage 17021
44.1%
own 2975
 
7.7%
other 98
 
0.3%
none 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
E 35602
16.2%
R 35599
16.2%
T 35599
16.2%
G 34042
15.5%
N 21461
9.8%
O 20097
9.2%
M 17021
7.8%
A 17021
7.8%
W 2975
 
1.4%
H 98
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 219515
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 35602
16.2%
R 35599
16.2%
T 35599
16.2%
G 34042
15.5%
N 21461
9.8%
O 20097
9.2%
M 17021
7.8%
A 17021
7.8%
W 2975
 
1.4%
H 98
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 219515
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 35602
16.2%
R 35599
16.2%
T 35599
16.2%
G 34042
15.5%
N 21461
9.8%
O 20097
9.2%
M 17021
7.8%
A 17021
7.8%
W 2975
 
1.4%
H 98
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 219515
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 35602
16.2%
R 35599
16.2%
T 35599
16.2%
G 34042
15.5%
N 21461
9.8%
O 20097
9.2%
M 17021
7.8%
A 17021
7.8%
W 2975
 
1.4%
H 98
 
< 0.1%

annual_inc
Real number (ℝ)

Distinct5215
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68777.974
Minimum4000
Maximum6000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size602.8 KiB
2023-02-05T19:47:25.673223image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum4000
5-th percentile24000
Q140000
median58868
Q382000
95-th percentile140004
Maximum6000000
Range5996000
Interquartile range (IQR)42000

Descriptive statistics

Standard deviation64218.682
Coefficient of variation (CV)0.93371
Kurtosis2308.7752
Mean68777.974
Median Absolute Deviation (MAD)19868
Skewness31.198414
Sum2.6532479 × 109
Variance4.1240391 × 109
MonotonicityNot monotonic
2023-02-05T19:47:25.961725image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60000 1466
 
3.8%
50000 1029
 
2.7%
40000 855
 
2.2%
45000 811
 
2.1%
30000 808
 
2.1%
75000 786
 
2.0%
65000 779
 
2.0%
70000 714
 
1.9%
48000 696
 
1.8%
80000 636
 
1.6%
Other values (5205) 29997
77.8%
ValueCountFrequency (%)
4000 1
 
< 0.1%
4080 1
 
< 0.1%
4200 2
 
< 0.1%
4800 4
< 0.1%
4888 1
 
< 0.1%
5000 1
 
< 0.1%
5500 1
 
< 0.1%
6000 5
< 0.1%
7000 1
 
< 0.1%
7200 4
< 0.1%
ValueCountFrequency (%)
6000000 1
 
< 0.1%
3900000 1
 
< 0.1%
2039784 1
 
< 0.1%
1900000 1
 
< 0.1%
1782000 1
 
< 0.1%
1440000 1
 
< 0.1%
1362000 1
 
< 0.1%
1250000 1
 
< 0.1%
1200000 4
< 0.1%
1176000 1
 
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size602.8 KiB
Not Verified
16694 
Verified
12206 
Source Verified
9677 

Length

Max length15
Median length12
Mean length11.486922
Min length8

Characters and Unicode

Total characters443131
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVerified
2nd rowSource Verified
3rd rowNot Verified
4th rowSource Verified
5th rowSource Verified

Common Values

ValueCountFrequency (%)
Not Verified 16694
43.3%
Verified 12206
31.6%
Source Verified 9677
25.1%

Length

2023-02-05T19:47:26.063309image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-05T19:47:26.166105image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
verified 38577
59.4%
not 16694
25.7%
source 9677
 
14.9%

Most occurring characters

ValueCountFrequency (%)
e 86831
19.6%
i 77154
17.4%
r 48254
10.9%
V 38577
8.7%
f 38577
8.7%
d 38577
8.7%
o 26371
 
6.0%
26371
 
6.0%
N 16694
 
3.8%
t 16694
 
3.8%
Other values (3) 29031
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 351812
79.4%
Uppercase Letter 64948
 
14.7%
Space Separator 26371
 
6.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 86831
24.7%
i 77154
21.9%
r 48254
13.7%
f 38577
11.0%
d 38577
11.0%
o 26371
 
7.5%
t 16694
 
4.7%
u 9677
 
2.8%
c 9677
 
2.8%
Uppercase Letter
ValueCountFrequency (%)
V 38577
59.4%
N 16694
25.7%
S 9677
 
14.9%
Space Separator
ValueCountFrequency (%)
26371
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 416760
94.0%
Common 26371
 
6.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 86831
20.8%
i 77154
18.5%
r 48254
11.6%
V 38577
9.3%
f 38577
9.3%
d 38577
9.3%
o 26371
 
6.3%
N 16694
 
4.0%
t 16694
 
4.0%
S 9677
 
2.3%
Other values (2) 19354
 
4.6%
Common
ValueCountFrequency (%)
26371
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 443131
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 86831
19.6%
i 77154
17.4%
r 48254
10.9%
V 38577
8.7%
f 38577
8.7%
d 38577
8.7%
o 26371
 
6.0%
26371
 
6.0%
N 16694
 
3.8%
t 16694
 
3.8%
Other values (3) 29031
 
6.6%

issue_d
Categorical

HIGH CARDINALITY  HIGH CORRELATION 

Distinct55
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size602.8 KiB
Nov-11
 
2062
Dec-11
 
2042
Oct-11
 
1941
Sep-11
 
1913
Aug-11
 
1798
Other values (50)
28821 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters231462
Distinct characters28
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowDec-11
2nd rowDec-11
3rd rowDec-11
4th rowDec-11
5th rowDec-11

Common Values

ValueCountFrequency (%)
Nov-11 2062
 
5.3%
Dec-11 2042
 
5.3%
Oct-11 1941
 
5.0%
Sep-11 1913
 
5.0%
Aug-11 1798
 
4.7%
Jul-11 1745
 
4.5%
Jun-11 1728
 
4.5%
May-11 1609
 
4.2%
Apr-11 1559
 
4.0%
Mar-11 1442
 
3.7%
Other values (45) 20738
53.8%

Length

2023-02-05T19:47:26.245656image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nov-11 2062
 
5.3%
dec-11 2042
 
5.3%
oct-11 1941
 
5.0%
sep-11 1913
 
5.0%
aug-11 1798
 
4.7%
jul-11 1745
 
4.5%
jun-11 1728
 
4.5%
may-11 1609
 
4.2%
apr-11 1559
 
4.0%
mar-11 1442
 
3.7%
Other values (45) 20738
53.8%

Most occurring characters

ValueCountFrequency (%)
1 52564
22.7%
- 38577
16.7%
0 18061
 
7.8%
e 10071
 
4.4%
u 9919
 
4.3%
J 8910
 
3.8%
a 7989
 
3.5%
c 7976
 
3.4%
p 6329
 
2.7%
A 6219
 
2.7%
Other values (18) 64847
28.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 77154
33.3%
Lowercase Letter 77154
33.3%
Dash Punctuation 38577
16.7%
Uppercase Letter 38577
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 10071
13.1%
u 9919
12.9%
a 7989
10.4%
c 7976
10.3%
p 6329
8.2%
n 5559
7.2%
r 5522
7.2%
o 4006
 
5.2%
v 4006
 
5.2%
t 3761
 
4.9%
Other values (4) 12016
15.6%
Uppercase Letter
ValueCountFrequency (%)
J 8910
23.1%
A 6219
16.1%
M 5610
14.5%
D 4215
10.9%
N 4006
10.4%
O 3761
9.7%
S 3498
 
9.1%
F 2358
 
6.1%
Decimal Number
ValueCountFrequency (%)
1 52564
68.1%
0 18061
 
23.4%
9 4716
 
6.1%
8 1562
 
2.0%
7 251
 
0.3%
Dash Punctuation
ValueCountFrequency (%)
- 38577
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 115731
50.0%
Latin 115731
50.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 10071
 
8.7%
u 9919
 
8.6%
J 8910
 
7.7%
a 7989
 
6.9%
c 7976
 
6.9%
p 6329
 
5.5%
A 6219
 
5.4%
M 5610
 
4.8%
n 5559
 
4.8%
r 5522
 
4.8%
Other values (12) 41627
36.0%
Common
ValueCountFrequency (%)
1 52564
45.4%
- 38577
33.3%
0 18061
 
15.6%
9 4716
 
4.1%
8 1562
 
1.3%
7 251
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 231462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 52564
22.7%
- 38577
16.7%
0 18061
 
7.8%
e 10071
 
4.4%
u 9919
 
4.3%
J 8910
 
3.8%
a 7989
 
3.5%
c 7976
 
3.4%
p 6329
 
2.7%
A 6219
 
2.7%
Other values (18) 64847
28.0%

loan_status
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size602.8 KiB
Fully Paid
32950 
Charged Off
5627 

Length

Max length11
Median length10
Mean length10.145864
Min length10

Characters and Unicode

Total characters391397
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFully Paid
2nd rowCharged Off
3rd rowFully Paid
4th rowFully Paid
5th rowFully Paid

Common Values

ValueCountFrequency (%)
Fully Paid 32950
85.4%
Charged Off 5627
 
14.6%

Length

2023-02-05T19:47:26.325404image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-05T19:47:26.415692image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
fully 32950
42.7%
paid 32950
42.7%
charged 5627
 
7.3%
off 5627
 
7.3%

Most occurring characters

ValueCountFrequency (%)
l 65900
16.8%
38577
9.9%
a 38577
9.9%
d 38577
9.9%
F 32950
8.4%
u 32950
8.4%
y 32950
8.4%
P 32950
8.4%
i 32950
8.4%
f 11254
 
2.9%
Other values (6) 33762
8.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 275666
70.4%
Uppercase Letter 77154
 
19.7%
Space Separator 38577
 
9.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 65900
23.9%
a 38577
14.0%
d 38577
14.0%
u 32950
12.0%
y 32950
12.0%
i 32950
12.0%
f 11254
 
4.1%
h 5627
 
2.0%
r 5627
 
2.0%
g 5627
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
F 32950
42.7%
P 32950
42.7%
C 5627
 
7.3%
O 5627
 
7.3%
Space Separator
ValueCountFrequency (%)
38577
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 352820
90.1%
Common 38577
 
9.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 65900
18.7%
a 38577
10.9%
d 38577
10.9%
F 32950
9.3%
u 32950
9.3%
y 32950
9.3%
P 32950
9.3%
i 32950
9.3%
f 11254
 
3.2%
C 5627
 
1.6%
Other values (5) 28135
8.0%
Common
ValueCountFrequency (%)
38577
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 391397
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 65900
16.8%
38577
9.9%
a 38577
9.9%
d 38577
9.9%
F 32950
8.4%
u 32950
8.4%
y 32950
8.4%
P 32950
8.4%
i 32950
8.4%
f 11254
 
2.9%
Other values (6) 33762
8.6%

pymnt_plan
Boolean

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size339.1 KiB
False
38577 
ValueCountFrequency (%)
False 38577
100.0%
2023-02-05T19:47:26.492665image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

url
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct38577
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size602.8 KiB
https://lendingclub.com/browse/loanDetail.action?loan_id=1077501
 
1
https://lendingclub.com/browse/loanDetail.action?loan_id=562133
 
1
https://lendingclub.com/browse/loanDetail.action?loan_id=558545
 
1
https://lendingclub.com/browse/loanDetail.action?loan_id=562256
 
1
https://lendingclub.com/browse/loanDetail.action?loan_id=562224
 
1
Other values (38572)
38572 

Length

Max length64
Median length63
Mean length63.102807
Min length62

Characters and Unicode

Total characters2434317
Distinct characters35
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38577 ?
Unique (%)100.0%

Sample

1st rowhttps://lendingclub.com/browse/loanDetail.action?loan_id=1077501
2nd rowhttps://lendingclub.com/browse/loanDetail.action?loan_id=1077430
3rd rowhttps://lendingclub.com/browse/loanDetail.action?loan_id=1077175
4th rowhttps://lendingclub.com/browse/loanDetail.action?loan_id=1076863
5th rowhttps://lendingclub.com/browse/loanDetail.action?loan_id=1075269

Common Values

ValueCountFrequency (%)
https://lendingclub.com/browse/loanDetail.action?loan_id=1077501 1
 
< 0.1%
https://lendingclub.com/browse/loanDetail.action?loan_id=562133 1
 
< 0.1%
https://lendingclub.com/browse/loanDetail.action?loan_id=558545 1
 
< 0.1%
https://lendingclub.com/browse/loanDetail.action?loan_id=562256 1
 
< 0.1%
https://lendingclub.com/browse/loanDetail.action?loan_id=562224 1
 
< 0.1%
https://lendingclub.com/browse/loanDetail.action?loan_id=561407 1
 
< 0.1%
https://lendingclub.com/browse/loanDetail.action?loan_id=562178 1
 
< 0.1%
https://lendingclub.com/browse/loanDetail.action?loan_id=562173 1
 
< 0.1%
https://lendingclub.com/browse/loanDetail.action?loan_id=560051 1
 
< 0.1%
https://lendingclub.com/browse/loanDetail.action?loan_id=561718 1
 
< 0.1%
Other values (38567) 38567
> 99.9%

Length

2023-02-05T19:47:26.571591image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://lendingclub.com/browse/loandetail.action?loan_id=1077501 1
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=1069357 1
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=1069742 1
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=1069039 1
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=1062474 1
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=1077175 1
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=1076863 1
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=1075269 1
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=1069639 1
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=1072053 1
 
< 0.1%
Other values (38567) 38567
> 99.9%

Most occurring characters

ValueCountFrequency (%)
o 192885
 
7.9%
l 192885
 
7.9%
n 192885
 
7.9%
a 154308
 
6.3%
t 154308
 
6.3%
/ 154308
 
6.3%
i 154308
 
6.3%
c 115731
 
4.8%
e 115731
 
4.8%
. 77154
 
3.2%
Other values (25) 929814
38.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1774542
72.9%
Other Punctuation 308616
 
12.7%
Decimal Number 235428
 
9.7%
Uppercase Letter 38577
 
1.6%
Connector Punctuation 38577
 
1.6%
Math Symbol 38577
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 192885
10.9%
l 192885
10.9%
n 192885
10.9%
a 154308
8.7%
t 154308
8.7%
i 154308
8.7%
c 115731
 
6.5%
e 115731
 
6.5%
b 77154
 
4.3%
d 77154
 
4.3%
Other values (8) 347193
19.6%
Decimal Number
ValueCountFrequency (%)
6 26013
11.0%
5 26004
11.0%
7 25234
10.7%
4 25013
10.6%
8 24746
10.5%
1 23280
9.9%
0 23016
9.8%
3 21489
9.1%
9 20951
8.9%
2 19682
8.4%
Other Punctuation
ValueCountFrequency (%)
/ 154308
50.0%
. 77154
25.0%
? 38577
 
12.5%
: 38577
 
12.5%
Uppercase Letter
ValueCountFrequency (%)
D 38577
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 38577
100.0%
Math Symbol
ValueCountFrequency (%)
= 38577
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1813119
74.5%
Common 621198
 
25.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 192885
10.6%
l 192885
10.6%
n 192885
10.6%
a 154308
 
8.5%
t 154308
 
8.5%
i 154308
 
8.5%
c 115731
 
6.4%
e 115731
 
6.4%
b 77154
 
4.3%
d 77154
 
4.3%
Other values (9) 385770
21.3%
Common
ValueCountFrequency (%)
/ 154308
24.8%
. 77154
12.4%
? 38577
 
6.2%
_ 38577
 
6.2%
= 38577
 
6.2%
: 38577
 
6.2%
6 26013
 
4.2%
5 26004
 
4.2%
7 25234
 
4.1%
4 25013
 
4.0%
Other values (6) 133164
21.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2434317
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 192885
 
7.9%
l 192885
 
7.9%
n 192885
 
7.9%
a 154308
 
6.3%
t 154308
 
6.3%
/ 154308
 
6.3%
i 154308
 
6.3%
c 115731
 
4.8%
e 115731
 
4.8%
. 77154
 
3.2%
Other values (25) 929814
38.2%

desc
Categorical

HIGH CARDINALITY  MISSING 

Distinct25803
Distinct (%)99.1%
Missing12527
Missing (%)32.5%
Memory size602.8 KiB
 
209
Debt Consolidation
 
8
Camping Membership
 
6
credit card debt consolidation
 
3
personal loan
 
3
Other values (25798)
25821 

Length

Max length3988
Median length2235
Mean length428.42656
Min length1

Characters and Unicode

Total characters11160512
Distinct characters142
Distinct categories17 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25777 ?
Unique (%)99.0%

Sample

1st row Borrower added on 12/22/11 > I need to upgrade my business technologies.<br>
2nd row Borrower added on 12/22/11 > I plan to use this money to finance the motorcycle i am looking at. I plan to have it paid off as soon as possible/when i sell my old bike. I only need this money because the deal im looking at is to good to pass up.<br><br> Borrower added on 12/22/11 > I plan to use this money to finance the motorcycle i am looking at. I plan to have it paid off as soon as possible/when i sell my old bike.I only need this money because the deal im looking at is to good to pass up. I have finished college with an associates degree in business and its takingmeplaces<br>
3rd row Borrower added on 12/21/11 > to pay for property tax (borrow from friend, need to pay back) & central A/C need to be replace. I'm very sorry to let my loan expired last time.<br>
4th row Borrower added on 12/18/11 > I am planning on using the funds to pay off two retail credit cards with 24.99% interest rates, as well as a major bank credit card with a 18.99% rate. I pay all my bills on time, looking for a lower combined payment and lower monthly payment.<br>
5th row Borrower added on 12/16/11 > Downpayment for a car.<br>

Common Values

ValueCountFrequency (%)
209
 
0.5%
Debt Consolidation 8
 
< 0.1%
Camping Membership 6
 
< 0.1%
credit card debt consolidation 3
 
< 0.1%
personal loan 3
 
< 0.1%
Personal Loan 3
 
< 0.1%
credit card consolidation 3
 
< 0.1%
Borrower added on 12/05/11 > Credit Card Refinancing<br> 2
 
< 0.1%
I have 2nd mortgage on a rental property with balance of $109k. I have cash flow of $85k and plan to pay off the 2nd mortgage using this loan to make up for rest of the balance. You will see a 2nd mortgage debt on my credit around $965.00 which will go away once this loan is approved as I will be paying it off. The interest rate needs to be below 9% since I can use my credit card to borrow same money for 10%. 2
 
< 0.1%
This loan would be to consolidate my credit card debts, and have one payment at a reasonable interest rate. 2
 
< 0.1%
Other values (25793) 25809
66.9%
(Missing) 12527
32.5%

Length

2023-02-05T19:47:26.684886image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
i 75916
 
3.8%
to 69499
 
3.5%
a 53846
 
2.7%
the 53177
 
2.7%
and 53117
 
2.6%
my 50208
 
2.5%
on 47613
 
2.4%
36046
 
1.8%
for 31977
 
1.6%
have 31738
 
1.6%
Other values (53153) 1501286
74.9%

Most occurring characters

ValueCountFrequency (%)
2073657
18.6%
e 932554
 
8.4%
a 698467
 
6.3%
o 692152
 
6.2%
t 635221
 
5.7%
n 598326
 
5.4%
r 574714
 
5.1%
i 485292
 
4.3%
s 416998
 
3.7%
d 388085
 
3.5%
Other values (132) 3665046
32.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7952052
71.3%
Space Separator 2073730
 
18.6%
Decimal Number 337501
 
3.0%
Other Punctuation 318845
 
2.9%
Uppercase Letter 296543
 
2.7%
Math Symbol 136215
 
1.2%
Currency Symbol 16427
 
0.1%
Dash Punctuation 12773
 
0.1%
Close Punctuation 7222
 
0.1%
Open Punctuation 6623
 
0.1%
Other values (7) 2581
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 92670
31.3%
B 33654
 
11.3%
T 27855
 
9.4%
A 15268
 
5.1%
C 14060
 
4.7%
M 14004
 
4.7%
S 9490
 
3.2%
E 9055
 
3.1%
W 8665
 
2.9%
L 8481
 
2.9%
Other values (21) 63341
21.4%
Lowercase Letter
ValueCountFrequency (%)
e 932554
11.7%
a 698467
 
8.8%
o 692152
 
8.7%
t 635221
 
8.0%
n 598326
 
7.5%
r 574714
 
7.2%
i 485292
 
6.1%
s 416998
 
5.2%
d 388085
 
4.9%
l 347474
 
4.4%
Other values (18) 2182769
27.4%
Other Punctuation
ValueCountFrequency (%)
. 117933
37.0%
/ 113038
35.5%
, 49105
15.4%
' 13124
 
4.1%
! 6589
 
2.1%
% 5598
 
1.8%
: 5223
 
1.6%
; 3253
 
1.0%
& 2537
 
0.8%
" 799
 
0.3%
Other values (10) 1646
 
0.5%
Control
ValueCountFrequency (%)
1283
60.4%
€ 411
 
19.4%
™ 191
 
9.0%
’ 37
 
1.7%
“ 37
 
1.7%
‚ 35
 
1.6%
 27
 
1.3%
ƒ 27
 
1.3%
œ 23
 
1.1%
š 15
 
0.7%
Other values (9) 37
 
1.7%
Decimal Number
ValueCountFrequency (%)
0 102100
30.3%
1 93833
27.8%
2 35795
 
10.6%
5 20973
 
6.2%
3 17490
 
5.2%
9 15923
 
4.7%
4 13450
 
4.0%
6 12849
 
3.8%
7 12558
 
3.7%
8 12530
 
3.7%
Math Symbol
ValueCountFrequency (%)
> 82133
60.3%
< 52206
38.3%
+ 959
 
0.7%
= 592
 
0.4%
~ 284
 
0.2%
¬ 31
 
< 0.1%
| 10
 
< 0.1%
Other Symbol
ValueCountFrequency (%)
¦ 96
83.5%
© 15
 
13.0%
2
 
1.7%
® 2
 
1.7%
Dash Punctuation
ValueCountFrequency (%)
- 12758
99.9%
9
 
0.1%
6
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 7176
99.4%
] 43
 
0.6%
} 3
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 6578
99.3%
[ 43
 
0.6%
{ 2
 
< 0.1%
Modifier Symbol
ValueCountFrequency (%)
` 13
68.4%
^ 5
 
26.3%
¯ 1
 
5.3%
Space Separator
ValueCountFrequency (%)
2073657
> 99.9%
  73
 
< 0.1%
Currency Symbol
ValueCountFrequency (%)
$ 16353
99.5%
¢ 74
 
0.5%
Final Punctuation
ValueCountFrequency (%)
78
81.2%
18
 
18.8%
Initial Punctuation
ValueCountFrequency (%)
18
85.7%
3
 
14.3%
Other Number
ValueCountFrequency (%)
½ 6
75.0%
¾ 2
 
25.0%
Connector Punctuation
ValueCountFrequency (%)
_ 199
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8248595
73.9%
Common 2911917
 
26.1%

Most frequent character per script

Common
ValueCountFrequency (%)
2073657
71.2%
. 117933
 
4.1%
/ 113038
 
3.9%
0 102100
 
3.5%
1 93833
 
3.2%
> 82133
 
2.8%
< 52206
 
1.8%
, 49105
 
1.7%
2 35795
 
1.2%
5 20973
 
0.7%
Other values (73) 171144
 
5.9%
Latin
ValueCountFrequency (%)
e 932554
 
11.3%
a 698467
 
8.5%
o 692152
 
8.4%
t 635221
 
7.7%
n 598326
 
7.3%
r 574714
 
7.0%
i 485292
 
5.9%
s 416998
 
5.1%
d 388085
 
4.7%
l 347474
 
4.2%
Other values (49) 2479312
30.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11158518
> 99.9%
None 1833
 
< 0.1%
Punctuation 159
 
< 0.1%
Specials 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2073657
18.6%
e 932554
 
8.4%
a 698467
 
6.3%
o 692152
 
6.2%
t 635221
 
5.7%
n 598326
 
5.4%
r 574714
 
5.2%
i 485292
 
4.3%
s 416998
 
3.7%
d 388085
 
3.5%
Other values (86) 3663052
32.8%
None
ValueCountFrequency (%)
â 438
23.9%
€ 411
22.4%
™ 191
10.4%
 127
 
6.9%
à 97
 
5.3%
¦ 96
 
5.2%
¢ 74
 
4.0%
  73
 
4.0%
’ 37
 
2.0%
“ 37
 
2.0%
Other values (27) 252
13.7%
Punctuation
ValueCountFrequency (%)
78
49.1%
19
 
11.9%
18
 
11.3%
18
 
11.3%
9
 
5.7%
8
 
5.0%
6
 
3.8%
3
 
1.9%
Specials
ValueCountFrequency (%)
2
100.0%

purpose
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size602.8 KiB
debt_consolidation
18055 
credit_card
5027 
other
3865 
home_improvement
2875 
major_purchase
2150 
Other values (9)
6605 

Length

Max length18
Median length16
Mean length13.726106
Min length3

Characters and Unicode

Total characters529512
Distinct characters22
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcredit_card
2nd rowcar
3rd rowsmall_business
4th rowother
5th rowwedding

Common Values

ValueCountFrequency (%)
debt_consolidation 18055
46.8%
credit_card 5027
 
13.0%
other 3865
 
10.0%
home_improvement 2875
 
7.5%
major_purchase 2150
 
5.6%
small_business 1754
 
4.5%
car 1499
 
3.9%
wedding 926
 
2.4%
medical 681
 
1.8%
moving 576
 
1.5%
Other values (4) 1169
 
3.0%

Length

2023-02-05T19:47:26.796185image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
debt_consolidation 18055
46.8%
credit_card 5027
 
13.0%
other 3865
 
10.0%
home_improvement 2875
 
7.5%
major_purchase 2150
 
5.6%
small_business 1754
 
4.5%
car 1499
 
3.9%
wedding 926
 
2.4%
medical 681
 
1.8%
moving 576
 
1.5%
Other values (4) 1169
 
3.0%

Most occurring characters

ValueCountFrequency (%)
o 67573
12.8%
d 49022
9.3%
i 48649
9.2%
t 48577
9.2%
n 43145
8.1%
e 42285
 
8.0%
c 33139
 
6.3%
a 32818
 
6.2%
_ 29963
 
5.7%
s 27588
 
5.2%
Other values (12) 106753
20.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 499549
94.3%
Connector Punctuation 29963
 
5.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 67573
13.5%
d 49022
9.8%
i 48649
9.7%
t 48577
9.7%
n 43145
8.6%
e 42285
8.5%
c 33139
 
6.6%
a 32818
 
6.6%
s 27588
 
5.5%
r 22797
 
4.6%
Other values (11) 83956
16.8%
Connector Punctuation
ValueCountFrequency (%)
_ 29963
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 499549
94.3%
Common 29963
 
5.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 67573
13.5%
d 49022
9.8%
i 48649
9.7%
t 48577
9.7%
n 43145
8.6%
e 42285
8.5%
c 33139
 
6.6%
a 32818
 
6.6%
s 27588
 
5.5%
r 22797
 
4.6%
Other values (11) 83956
16.8%
Common
ValueCountFrequency (%)
_ 29963
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 529512
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 67573
12.8%
d 49022
9.3%
i 48649
9.2%
t 48577
9.2%
n 43145
8.1%
e 42285
 
8.0%
c 33139
 
6.3%
a 32818
 
6.2%
_ 29963
 
5.7%
s 27588
 
5.2%
Other values (12) 106753
20.2%

title
Categorical

Distinct19297
Distinct (%)50.0%
Missing11
Missing (%)< 0.1%
Memory size602.8 KiB
Debt Consolidation
 
2090
Debt Consolidation Loan
 
1620
Personal Loan
 
641
Consolidation
 
491
debt consolidation
 
478
Other values (19292)
33246 

Length

Max length80
Median length72
Mean length17.231888
Min length1

Characters and Unicode

Total characters664565
Distinct characters108
Distinct categories15 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17349 ?
Unique (%)45.0%

Sample

1st rowComputer
2nd rowbike
3rd rowreal estate business
4th rowpersonel
5th rowMy wedding loan I promise to pay back

Common Values

ValueCountFrequency (%)
Debt Consolidation 2090
 
5.4%
Debt Consolidation Loan 1620
 
4.2%
Personal Loan 641
 
1.7%
Consolidation 491
 
1.3%
debt consolidation 478
 
1.2%
Credit Card Consolidation 348
 
0.9%
Home Improvement 345
 
0.9%
Debt consolidation 322
 
0.8%
Small Business Loan 305
 
0.8%
Personal 301
 
0.8%
Other values (19287) 31625
82.0%

Length

2023-02-05T19:47:26.912496image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
loan 10487
 
10.3%
debt 8915
 
8.7%
consolidation 8276
 
8.1%
credit 4503
 
4.4%
card 3264
 
3.2%
personal 1983
 
1.9%
home 1801
 
1.8%
pay 1314
 
1.3%
off 1230
 
1.2%
my 1113
 
1.1%
Other values (8837) 59041
57.9%

Most occurring characters

ValueCountFrequency (%)
64502
 
9.7%
o 63688
 
9.6%
n 54040
 
8.1%
e 53270
 
8.0%
a 48795
 
7.3%
i 42606
 
6.4%
t 41486
 
6.2%
d 29847
 
4.5%
r 28521
 
4.3%
s 27739
 
4.2%
Other values (98) 210071
31.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 507482
76.4%
Uppercase Letter 80935
 
12.2%
Space Separator 64502
 
9.7%
Decimal Number 5844
 
0.9%
Other Punctuation 4393
 
0.7%
Dash Punctuation 815
 
0.1%
Connector Punctuation 204
 
< 0.1%
Close Punctuation 103
 
< 0.1%
Currency Symbol 93
 
< 0.1%
Math Symbol 91
 
< 0.1%
Other values (5) 103
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 63688
12.5%
n 54040
10.6%
e 53270
10.5%
a 48795
9.6%
i 42606
8.4%
t 41486
8.2%
d 29847
 
5.9%
r 28521
 
5.6%
s 27739
 
5.5%
l 25529
 
5.0%
Other values (18) 91961
18.1%
Uppercase Letter
ValueCountFrequency (%)
C 17977
22.2%
L 9952
12.3%
D 8927
11.0%
P 5506
 
6.8%
R 3665
 
4.5%
M 3178
 
3.9%
S 3132
 
3.9%
B 3018
 
3.7%
H 2826
 
3.5%
E 2812
 
3.5%
Other values (18) 19942
24.6%
Other Punctuation
ValueCountFrequency (%)
! 1105
25.2%
' 980
22.3%
. 709
16.1%
/ 519
11.8%
, 431
 
9.8%
& 325
 
7.4%
% 95
 
2.2%
: 64
 
1.5%
" 56
 
1.3%
? 25
 
0.6%
Other values (5) 84
 
1.9%
Decimal Number
ValueCountFrequency (%)
0 1639
28.0%
1 1638
28.0%
2 1077
18.4%
3 291
 
5.0%
5 252
 
4.3%
9 250
 
4.3%
4 212
 
3.6%
6 174
 
3.0%
8 163
 
2.8%
7 148
 
2.5%
Control
ValueCountFrequency (%)
€ 4
21.1%
— 4
21.1%
 4
21.1%
™ 2
10.5%
2
10.5%
… 1
 
5.3%
– 1
 
5.3%
‚ 1
 
5.3%
Math Symbol
ValueCountFrequency (%)
+ 52
57.1%
= 19
 
20.9%
< 9
 
9.9%
> 8
 
8.8%
~ 2
 
2.2%
| 1
 
1.1%
Modifier Symbol
ValueCountFrequency (%)
^ 1
33.3%
` 1
33.3%
´ 1
33.3%
Close Punctuation
ValueCountFrequency (%)
) 99
96.1%
] 4
 
3.9%
Open Punctuation
ValueCountFrequency (%)
( 75
96.2%
[ 3
 
3.8%
Space Separator
ValueCountFrequency (%)
64502
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 815
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 204
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 93
100.0%
Other Symbol
ValueCountFrequency (%)
¦ 2
100.0%
Other Number
ValueCountFrequency (%)
³ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 588417
88.5%
Common 76148
 
11.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 63688
 
10.8%
n 54040
 
9.2%
e 53270
 
9.1%
a 48795
 
8.3%
i 42606
 
7.2%
t 41486
 
7.1%
d 29847
 
5.1%
r 28521
 
4.8%
s 27739
 
4.7%
l 25529
 
4.3%
Other values (46) 172896
29.4%
Common
ValueCountFrequency (%)
64502
84.7%
0 1639
 
2.2%
1 1638
 
2.2%
! 1105
 
1.5%
2 1077
 
1.4%
' 980
 
1.3%
- 815
 
1.1%
. 709
 
0.9%
/ 519
 
0.7%
, 431
 
0.6%
Other values (42) 2733
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 664533
> 99.9%
None 32
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
64502
 
9.7%
o 63688
 
9.6%
n 54040
 
8.1%
e 53270
 
8.0%
a 48795
 
7.3%
i 42606
 
6.4%
t 41486
 
6.2%
d 29847
 
4.5%
r 28521
 
4.3%
s 27739
 
4.2%
Other values (84) 210039
31.6%
None
ValueCountFrequency (%)
î 4
12.5%
â 4
12.5%
€ 4
12.5%
— 4
12.5%
 4
12.5%
à 2
6.2%
¦ 2
6.2%
™ 2
6.2%
… 1
 
3.1%
³ 1
 
3.1%
Other values (4) 4
12.5%

zip_code
Categorical

Distinct822
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size602.8 KiB
100xx
 
583
945xx
 
531
112xx
 
502
606xx
 
493
070xx
 
455
Other values (817)
36013 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters192885
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique57 ?
Unique (%)0.1%

Sample

1st row860xx
2nd row309xx
3rd row606xx
4th row917xx
5th row852xx

Common Values

ValueCountFrequency (%)
100xx 583
 
1.5%
945xx 531
 
1.4%
112xx 502
 
1.3%
606xx 493
 
1.3%
070xx 455
 
1.2%
900xx 446
 
1.2%
021xx 383
 
1.0%
300xx 383
 
1.0%
926xx 366
 
0.9%
750xx 356
 
0.9%
Other values (812) 34079
88.3%

Length

2023-02-05T19:47:27.011511image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
100xx 583
 
1.5%
945xx 531
 
1.4%
112xx 502
 
1.3%
606xx 493
 
1.3%
070xx 455
 
1.2%
900xx 446
 
1.2%
021xx 383
 
1.0%
300xx 383
 
1.0%
926xx 366
 
0.9%
750xx 356
 
0.9%
Other values (812) 34079
88.3%

Most occurring characters

ValueCountFrequency (%)
x 77154
40.0%
0 19225
 
10.0%
1 15171
 
7.9%
2 13183
 
6.8%
9 12359
 
6.4%
3 11987
 
6.2%
7 9950
 
5.2%
4 8860
 
4.6%
5 8765
 
4.5%
8 8425
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 115731
60.0%
Lowercase Letter 77154
40.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19225
16.6%
1 15171
13.1%
2 13183
11.4%
9 12359
10.7%
3 11987
10.4%
7 9950
8.6%
4 8860
7.7%
5 8765
7.6%
8 8425
7.3%
6 7806
6.7%
Lowercase Letter
ValueCountFrequency (%)
x 77154
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 115731
60.0%
Latin 77154
40.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19225
16.6%
1 15171
13.1%
2 13183
11.4%
9 12359
10.7%
3 11987
10.4%
7 9950
8.6%
4 8860
7.7%
5 8765
7.6%
8 8425
7.3%
6 7806
6.7%
Latin
ValueCountFrequency (%)
x 77154
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 192885
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
x 77154
40.0%
0 19225
 
10.0%
1 15171
 
7.9%
2 13183
 
6.8%
9 12359
 
6.4%
3 11987
 
6.2%
7 9950
 
5.2%
4 8860
 
4.6%
5 8765
 
4.5%
8 8425
 
4.4%

addr_state
Categorical

Distinct50
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size602.8 KiB
CA
6949 
NY
3698 
FL
2781 
TX
2659 
NJ
 
1790
Other values (45)
20700 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters77154
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAZ
2nd rowGA
3rd rowIL
4th rowCA
5th rowAZ

Common Values

ValueCountFrequency (%)
CA 6949
18.0%
NY 3698
 
9.6%
FL 2781
 
7.2%
TX 2659
 
6.9%
NJ 1790
 
4.6%
IL 1478
 
3.8%
PA 1468
 
3.8%
VA 1369
 
3.5%
GA 1359
 
3.5%
MA 1297
 
3.4%
Other values (40) 13729
35.6%

Length

2023-02-05T19:47:27.081791image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca 6949
18.0%
ny 3698
 
9.6%
fl 2781
 
7.2%
tx 2659
 
6.9%
nj 1790
 
4.6%
il 1478
 
3.8%
pa 1468
 
3.8%
va 1369
 
3.5%
ga 1359
 
3.5%
ma 1297
 
3.4%
Other values (40) 13729
35.6%

Most occurring characters

ValueCountFrequency (%)
A 15289
19.8%
C 9861
12.8%
N 7702
10.0%
L 5121
 
6.6%
M 4587
 
5.9%
Y 4089
 
5.3%
T 3790
 
4.9%
O 3336
 
4.3%
I 3002
 
3.9%
F 2781
 
3.6%
Other values (14) 17596
22.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 77154
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 15289
19.8%
C 9861
12.8%
N 7702
10.0%
L 5121
 
6.6%
M 4587
 
5.9%
Y 4089
 
5.3%
T 3790
 
4.9%
O 3336
 
4.3%
I 3002
 
3.9%
F 2781
 
3.6%
Other values (14) 17596
22.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 77154
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 15289
19.8%
C 9861
12.8%
N 7702
10.0%
L 5121
 
6.6%
M 4587
 
5.9%
Y 4089
 
5.3%
T 3790
 
4.9%
O 3336
 
4.3%
I 3002
 
3.9%
F 2781
 
3.6%
Other values (14) 17596
22.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 77154
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 15289
19.8%
C 9861
12.8%
N 7702
10.0%
L 5121
 
6.6%
M 4587
 
5.9%
Y 4089
 
5.3%
T 3790
 
4.9%
O 3336
 
4.3%
I 3002
 
3.9%
F 2781
 
3.6%
Other values (14) 17596
22.8%

dti
Real number (ℝ)

Distinct2853
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.272727
Minimum0
Maximum29.99
Zeros178
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size602.8 KiB
2023-02-05T19:47:27.167001image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.1
Q18.13
median13.37
Q318.56
95-th percentile23.8
Maximum29.99
Range29.99
Interquartile range (IQR)10.43

Descriptive statistics

Standard deviation6.6730443
Coefficient of variation (CV)0.50276362
Kurtosis-0.85629801
Mean13.272727
Median Absolute Deviation (MAD)5.21
Skewness-0.026842422
Sum512021.99
Variance44.52952
MonotonicityNot monotonic
2023-02-05T19:47:27.274075image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 178
 
0.5%
12 46
 
0.1%
18 45
 
0.1%
19.2 39
 
0.1%
13.2 39
 
0.1%
12.48 37
 
0.1%
16.8 37
 
0.1%
15 36
 
0.1%
6 36
 
0.1%
13.5 36
 
0.1%
Other values (2843) 38048
98.6%
ValueCountFrequency (%)
0 178
0.5%
0.01 3
 
< 0.1%
0.02 5
 
< 0.1%
0.03 2
 
< 0.1%
0.04 3
 
< 0.1%
0.05 2
 
< 0.1%
0.06 1
 
< 0.1%
0.07 5
 
< 0.1%
0.08 5
 
< 0.1%
0.09 2
 
< 0.1%
ValueCountFrequency (%)
29.99 1
 
< 0.1%
29.93 3
< 0.1%
29.92 2
< 0.1%
29.89 1
 
< 0.1%
29.88 1
 
< 0.1%
29.86 2
< 0.1%
29.85 1
 
< 0.1%
29.82 1
 
< 0.1%
29.79 1
 
< 0.1%
29.78 1
 
< 0.1%

mths_since_last_delinq
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct95
Distinct (%)0.7%
Missing24905
Missing (%)64.6%
Infinite0
Infinite (%)0.0%
Mean35.882534
Minimum0
Maximum120
Zeros443
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size602.8 KiB
2023-02-05T19:47:27.399891image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q118
median34
Q352
95-th percentile75
Maximum120
Range120
Interquartile range (IQR)34

Descriptive statistics

Standard deviation22.028093
Coefficient of variation (CV)0.61389458
Kurtosis-0.84497107
Mean35.882534
Median Absolute Deviation (MAD)17
Skewness0.30310156
Sum490586
Variance485.23688
MonotonicityNot monotonic
2023-02-05T19:47:27.540038image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 443
 
1.1%
15 244
 
0.6%
30 243
 
0.6%
23 243
 
0.6%
24 236
 
0.6%
38 233
 
0.6%
19 232
 
0.6%
22 228
 
0.6%
20 226
 
0.6%
18 225
 
0.6%
Other values (85) 11119
28.8%
(Missing) 24905
64.6%
ValueCountFrequency (%)
0 443
1.1%
1 29
 
0.1%
2 101
 
0.3%
3 143
 
0.4%
4 147
 
0.4%
5 145
 
0.4%
6 188
0.5%
7 172
 
0.4%
8 165
 
0.4%
9 171
 
0.4%
ValueCountFrequency (%)
120 1
< 0.1%
115 1
< 0.1%
107 1
< 0.1%
106 1
< 0.1%
103 1
< 0.1%
97 1
< 0.1%
96 1
< 0.1%
95 1
< 0.1%
89 1
< 0.1%
86 2
< 0.1%

mths_since_last_record
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct111
Distinct (%)4.1%
Missing35837
Missing (%)92.9%
Infinite0
Infinite (%)0.0%
Mean69.260949
Minimum0
Maximum129
Zeros670
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size602.8 KiB
2023-02-05T19:47:27.656845image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q119.75
median90
Q3104
95-th percentile115
Maximum129
Range129
Interquartile range (IQR)84.25

Descriptive statistics

Standard deviation43.987761
Coefficient of variation (CV)0.63510191
Kurtosis-1.1890678
Mean69.260949
Median Absolute Deviation (MAD)20
Skewness-0.69859329
Sum189775
Variance1934.9231
MonotonicityNot monotonic
2023-02-05T19:47:27.765402image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 670
 
1.7%
104 60
 
0.2%
113 58
 
0.2%
89 57
 
0.1%
111 56
 
0.1%
94 54
 
0.1%
108 54
 
0.1%
87 53
 
0.1%
100 53
 
0.1%
110 52
 
0.1%
Other values (101) 1573
 
4.1%
(Missing) 35837
92.9%
ValueCountFrequency (%)
0 670
1.7%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
11 2
 
< 0.1%
12 1
 
< 0.1%
13 2
 
< 0.1%
14 1
 
< 0.1%
17 3
 
< 0.1%
18 2
 
< 0.1%
ValueCountFrequency (%)
129 1
 
< 0.1%
120 1
 
< 0.1%
119 9
 
< 0.1%
118 36
0.1%
117 43
0.1%
116 41
0.1%
115 36
0.1%
114 50
0.1%
113 58
0.2%
112 39
0.1%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size339.1 KiB
False
38577 
ValueCountFrequency (%)
False 38577
100.0%
2023-02-05T19:47:27.867560image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

next_pymnt_d
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB
Distinct1
Distinct (%)< 0.1%
Missing56
Missing (%)0.1%
Memory size602.8 KiB
0.0
38521 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters115563
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 38521
99.9%
(Missing) 56
 
0.1%

Length

2023-02-05T19:47:27.932506image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-05T19:47:28.011465image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 38521
100.0%

Most occurring characters

ValueCountFrequency (%)
0 77042
66.7%
. 38521
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 77042
66.7%
Other Punctuation 38521
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 77042
100.0%
Other Punctuation
ValueCountFrequency (%)
. 38521
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 115563
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 77042
66.7%
. 38521
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 115563
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 77042
66.7%
. 38521
33.3%

mths_since_last_major_derog
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

policy_code
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size602.8 KiB
1
38577 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters38577
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 38577
100.0%

Length

2023-02-05T19:47:28.074949image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-05T19:47:28.151947image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 38577
100.0%

Most occurring characters

ValueCountFrequency (%)
1 38577
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 38577
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 38577
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 38577
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 38577
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38577
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 38577
100.0%

annual_inc_joint
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

dti_joint
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

verification_status_joint
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

acc_now_delinq
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size602.8 KiB
0
38577 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters38577
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 38577
100.0%

Length

2023-02-05T19:47:28.217628image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-05T19:47:28.295082image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 38577
100.0%

Most occurring characters

ValueCountFrequency (%)
0 38577
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 38577
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 38577
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 38577
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 38577
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38577
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 38577
100.0%

tot_coll_amt
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

tot_cur_bal
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

open_acc_6m
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

open_il_6m
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

open_il_12m
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

open_il_24m
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

mths_since_rcnt_il
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

total_bal_il
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

il_util
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

open_rv_12m
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

open_rv_24m
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

max_bal_bc
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

all_util
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

total_rev_hi_lim
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

inq_fi
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

total_cu_tl
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

inq_last_12m
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

acc_open_past_24mths
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

avg_cur_bal
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

bc_open_to_buy
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

bc_util
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB
Distinct1
Distinct (%)< 0.1%
Missing56
Missing (%)0.1%
Memory size602.8 KiB
0.0
38521 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters115563
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 38521
99.9%
(Missing) 56
 
0.1%

Length

2023-02-05T19:47:28.360404image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-05T19:47:28.438489image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 38521
100.0%

Most occurring characters

ValueCountFrequency (%)
0 77042
66.7%
. 38521
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 77042
66.7%
Other Punctuation 38521
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 77042
100.0%
Other Punctuation
ValueCountFrequency (%)
. 38521
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 115563
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 77042
66.7%
. 38521
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 115563
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 77042
66.7%
. 38521
33.3%

delinq_amnt
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size602.8 KiB
0
38577 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters38577
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 38577
100.0%

Length

2023-02-05T19:47:28.507334image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-05T19:47:28.607984image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 38577
100.0%

Most occurring characters

ValueCountFrequency (%)
0 38577
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 38577
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 38577
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 38577
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 38577
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38577
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 38577
100.0%

mo_sin_old_il_acct
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

mo_sin_old_rev_tl_op
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

mo_sin_rcnt_rev_tl_op
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

mo_sin_rcnt_tl
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

mort_acc
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

mths_since_recent_bc
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

mths_since_recent_bc_dlq
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

mths_since_recent_inq
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

mths_since_recent_revol_delinq
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

num_accts_ever_120_pd
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

num_actv_bc_tl
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

num_actv_rev_tl
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

num_bc_sats
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

num_bc_tl
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

num_il_tl
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

num_op_rev_tl
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

num_rev_accts
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

num_rev_tl_bal_gt_0
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

num_sats
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

num_tl_120dpd_2m
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

num_tl_30dpd
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

num_tl_90g_dpd_24m
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

num_tl_op_past_12m
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

pct_tl_nvr_dlq
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

percent_bc_gt_75
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

pub_rec_bankruptcies
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing697
Missing (%)1.8%
Memory size602.8 KiB
0.0
36238 
1.0
 
1637
2.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters113640
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 36238
93.9%
1.0 1637
 
4.2%
2.0 5
 
< 0.1%
(Missing) 697
 
1.8%

Length

2023-02-05T19:47:28.673054image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-05T19:47:28.761701image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 36238
95.7%
1.0 1637
 
4.3%
2.0 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 74118
65.2%
. 37880
33.3%
1 1637
 
1.4%
2 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 75760
66.7%
Other Punctuation 37880
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 74118
97.8%
1 1637
 
2.2%
2 5
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 37880
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 113640
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 74118
65.2%
. 37880
33.3%
1 1637
 
1.4%
2 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 113640
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 74118
65.2%
. 37880
33.3%
1 1637
 
1.4%
2 5
 
< 0.1%

tax_liens
Categorical

Distinct1
Distinct (%)< 0.1%
Missing39
Missing (%)0.1%
Memory size602.8 KiB
0.0
38538 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters115614
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 38538
99.9%
(Missing) 39
 
0.1%

Length

2023-02-05T19:47:28.832793image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-05T19:47:28.924369image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 38538
100.0%

Most occurring characters

ValueCountFrequency (%)
0 77076
66.7%
. 38538
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 77076
66.7%
Other Punctuation 38538
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 77076
100.0%
Other Punctuation
ValueCountFrequency (%)
. 38538
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 115614
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 77076
66.7%
. 38538
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 115614
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 77076
66.7%
. 38538
33.3%

tot_hi_cred_lim
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

total_bal_ex_mort
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

total_bc_limit
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

total_il_high_credit_limit
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing38577
Missing (%)100.0%
Memory size602.8 KiB

Interactions

2023-02-05T19:47:20.546706image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:11.691625image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:12.630661image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:13.579987image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:14.673640image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:15.632183image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:16.605102image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:17.568508image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:18.673851image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:19.613108image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:20.635573image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:11.786312image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:12.714996image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:13.796117image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:14.763753image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:15.725350image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:16.694126image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:17.656400image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:18.765315image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:19.699181image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:20.726198image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:11.876725image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:12.806385image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:13.892331image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:14.860477image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:15.826494image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:16.785249image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:17.747630image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:18.859472image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:19.791314image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:20.822055image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:11.976661image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:12.909851image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:13.989444image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:14.957470image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:15.925579image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:16.887781image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:17.842976image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:18.957444image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:19.887453image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:20.915116image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:12.072653image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:13.006360image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:14.089213image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:15.057351image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:16.023147image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:16.985416image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:17.942340image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:19.055770image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:19.982710image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:21.008259image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:12.167544image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:13.103079image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:14.186509image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:15.153864image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:16.123843image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:17.083911image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:18.038185image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:19.154073image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:20.079335image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:21.101182image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:12.259235image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:13.202906image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:14.291134image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:15.251158image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:16.221190image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:17.175558image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:18.139743image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:19.249916image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:20.171849image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:21.203951image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:12.365846image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:13.304455image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:14.390883image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:15.349430image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:16.320308image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:17.279603image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:18.235380image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:19.345658image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:20.264388image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:21.291013image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:12.453864image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:13.394719image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:14.482691image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:15.441453image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:16.416379image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:17.383280image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:18.326106image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:19.433867image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:20.355976image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:21.377733image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:12.538914image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:13.484572image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:14.574772image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:15.532976image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:16.508595image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:17.474252image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:18.414616image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:19.522809image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-02-05T19:47:20.456534image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-02-05T19:47:29.019720image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
idmember_idloan_amntfunded_amntfunded_amnt_invinstallmentannual_incdtimths_since_last_delinqmths_since_last_recordtermgradesub_gradeemp_lengthhome_ownershipverification_statusissue_dloan_statuspurposeaddr_statepub_rec_bankruptcies
id1.0000.9990.0900.1000.2160.0620.0380.0840.0970.5980.2920.0520.0710.0550.0820.2340.9150.0510.0730.0910.072
member_id0.9991.0000.0890.0990.2150.0620.0370.0840.0970.5990.3060.0540.0720.0550.0810.2520.9240.0510.0740.0990.045
loan_amnt0.0900.0891.0000.9910.9320.9590.4270.0700.0230.0180.3490.1360.1240.0510.0860.3070.0800.0660.1160.0200.025
funded_amnt0.1000.0990.9911.0000.9430.9730.4230.0700.0240.0140.3280.1350.1210.0510.0830.3010.0810.0630.1150.0160.025
funded_amnt_inv0.2160.2150.9320.9431.0000.9090.4000.0790.0980.4590.3490.1290.1150.0560.0820.3080.1480.0540.1080.0250.027
installment0.0620.0620.9590.9730.9091.0000.4190.0640.010-0.0210.1340.1350.1220.0430.0680.2670.0760.0390.1130.0150.021
annual_inc0.0380.0370.4270.4230.4000.4191.000-0.102-0.0040.0350.0000.0000.0000.0050.0000.0020.0190.0000.0000.0160.000
dti0.0840.0840.0700.0700.0790.064-0.1021.0000.0660.1710.0770.0630.0570.0170.0250.0700.0430.0470.0820.0330.016
mths_since_last_delinq0.0970.0970.0230.0240.0980.010-0.0040.0661.0000.5180.0390.0390.0350.0320.0250.0560.1050.0290.0050.0410.021
mths_since_last_record0.5980.5990.0180.0140.459-0.0210.0350.1710.5181.0000.2910.0820.0930.0940.0660.3360.3240.0510.0610.1000.658
term0.2920.3060.3490.3280.3490.1340.0000.0770.0390.2911.0000.4410.4750.1060.1040.2480.3430.1730.1120.0470.015
grade0.0520.0540.1360.1350.1290.1350.0000.0630.0390.0820.4411.0001.0000.0170.0510.1370.0700.2020.0690.0150.067
sub_grade0.0710.0720.1240.1210.1150.1220.0000.0570.0350.0930.4751.0001.0000.0180.0570.1450.0430.2070.0530.0070.072
emp_length0.0550.0550.0510.0510.0560.0430.0050.0170.0320.0940.1060.0170.0181.0000.1320.0810.0590.0180.0360.0220.044
home_ownership0.0820.0810.0860.0830.0820.0680.0000.0250.0250.0660.1040.0510.0570.1321.0000.0740.1330.0220.1250.1340.021
verification_status0.2340.2520.3070.3010.3080.2670.0020.0700.0560.3360.2480.1370.1450.0810.0741.0000.2880.0480.0950.0390.007
issue_d0.9150.9240.0800.0810.1480.0760.0190.0430.1050.3240.3430.0700.0430.0590.1330.2881.0000.0640.0730.0600.083
loan_status0.0510.0510.0660.0630.0540.0390.0000.0470.0290.0510.1730.2020.2070.0180.0220.0480.0641.0000.0970.0530.047
purpose0.0730.0740.1160.1150.1080.1130.0000.0820.0050.0610.1120.0690.0530.0360.1250.0950.0730.0971.0000.0320.023
addr_state0.0910.0990.0200.0160.0250.0150.0160.0330.0410.1000.0470.0150.0070.0220.1340.0390.0600.0530.0321.0000.047
pub_rec_bankruptcies0.0720.0450.0250.0250.0270.0210.0000.0160.0210.6580.0150.0670.0720.0440.0210.0070.0830.0470.0230.0471.000

Missing values

2023-02-05T19:47:21.719072image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-05T19:47:22.499197image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-02-05T19:47:23.267873image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idmember_idloan_amntfunded_amntfunded_amnt_invtermint_rateinstallmentgradesub_gradeemp_titleemp_lengthhome_ownershipannual_incverification_statusissue_dloan_statuspymnt_planurldescpurposetitlezip_codeaddr_statedtimths_since_last_delinqmths_since_last_recordinitial_list_statusnext_pymnt_dcollections_12_mths_ex_medmths_since_last_major_derogpolicy_codeannual_inc_jointdti_jointverification_status_jointacc_now_delinqtot_coll_amttot_cur_balopen_acc_6mopen_il_6mopen_il_12mopen_il_24mmths_since_rcnt_iltotal_bal_ilil_utilopen_rv_12mopen_rv_24mmax_bal_bcall_utiltotal_rev_hi_liminq_fitotal_cu_tlinq_last_12macc_open_past_24mthsavg_cur_balbc_open_to_buybc_utilchargeoff_within_12_mthsdelinq_amntmo_sin_old_il_acctmo_sin_old_rev_tl_opmo_sin_rcnt_rev_tl_opmo_sin_rcnt_tlmort_accmths_since_recent_bcmths_since_recent_bc_dlqmths_since_recent_inqmths_since_recent_revol_delinqnum_accts_ever_120_pdnum_actv_bc_tlnum_actv_rev_tlnum_bc_satsnum_bc_tlnum_il_tlnum_op_rev_tlnum_rev_acctsnum_rev_tl_bal_gt_0num_satsnum_tl_120dpd_2mnum_tl_30dpdnum_tl_90g_dpd_24mnum_tl_op_past_12mpct_tl_nvr_dlqpercent_bc_gt_75pub_rec_bankruptciestax_lienstot_hi_cred_limtotal_bal_ex_morttotal_bc_limittotal_il_high_credit_limit
010775011296599500050004975.036 months10.65%162.87BB2NaN10+ yearsRENT24000.0VerifiedDec-11Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=1077501Borrower added on 12/22/11 > I need to upgrade my business technologies.<br>credit_cardComputer860xxAZ27.65NaNNaNfNaN0.0NaN1NaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNNaNNaN
110774301314167250025002500.060 months15.27%59.83CC4Ryder< 1 yearRENT30000.0Source VerifiedDec-11Charged Offnhttps://lendingclub.com/browse/loanDetail.action?loan_id=1077430Borrower added on 12/22/11 > I plan to use this money to finance the motorcycle i am looking at. I plan to have it paid off as soon as possible/when i sell my old bike. I only need this money because the deal im looking at is to good to pass up.<br><br> Borrower added on 12/22/11 > I plan to use this money to finance the motorcycle i am looking at. I plan to have it paid off as soon as possible/when i sell my old bike.I only need this money because the deal im looking at is to good to pass up. I have finished college with an associates degree in business and its takingmeplaces<br>carbike309xxGA1.00NaNNaNfNaN0.0NaN1NaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNNaNNaN
210771751313524240024002400.036 months15.96%84.33CC5NaN10+ yearsRENT12252.0Not VerifiedDec-11Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=1077175NaNsmall_businessreal estate business606xxIL8.72NaNNaNfNaN0.0NaN1NaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNNaNNaN
310768631277178100001000010000.036 months13.49%339.31CC1AIR RESOURCES BOARD10+ yearsRENT49200.0Source VerifiedDec-11Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=1076863Borrower added on 12/21/11 > to pay for property tax (borrow from friend, need to pay back) & central A/C need to be replace. I'm very sorry to let my loan expired last time.<br>otherpersonel917xxCA20.0035.0NaNfNaN0.0NaN1NaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNNaNNaN
510752691311441500050005000.036 months7.90%156.46AA4Veolia Transportaton3 yearsRENT36000.0Source VerifiedDec-11Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=1075269NaNweddingMy wedding loan I promise to pay back852xxAZ11.20NaNNaNfNaN0.0NaN1NaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNNaNNaN
610696391304742700070007000.060 months15.96%170.08CC5Southern Star Photography8 yearsRENT47004.0Not VerifiedDec-11Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=1069639Borrower added on 12/18/11 > I am planning on using the funds to pay off two retail credit cards with 24.99% interest rates, as well as a major bank credit card with a 18.99% rate. I pay all my bills on time, looking for a lower combined payment and lower monthly payment.<br>debt_consolidationLoan280xxNC23.51NaNNaNfNaN0.0NaN1NaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNNaNNaN
710720531288686300030003000.036 months18.64%109.43EE1MKC Accounting9 yearsRENT48000.0Source VerifiedDec-11Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=1072053Borrower added on 12/16/11 > Downpayment for a car.<br>carCar Downpayment900xxCA5.35NaNNaNfNaN0.0NaN1NaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNNaNNaN
810717951306957560056005600.060 months21.28%152.39FF2NaN4 yearsOWN40000.0Source VerifiedDec-11Charged Offnhttps://lendingclub.com/browse/loanDetail.action?loan_id=1071795Borrower added on 12/21/11 > I own a small home-based judgment collection business. I have 5 years experience collecting debts. I am now going from a home office to a small office. I also plan to buy a small debt portfolio (eg. $10K for $1M of debt) <br>My score is not A+ because I own my home and have no mortgage.<br>small_businessExpand Business & Buy Debt Portfolio958xxCA5.55NaNNaNfNaN0.0NaN1NaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNNaNNaN
910715701306721537553755350.060 months12.69%121.45BB5Starbucks< 1 yearRENT15000.0VerifiedDec-11Charged Offnhttps://lendingclub.com/browse/loanDetail.action?loan_id=1071570Borrower added on 12/16/11 > I'm trying to build up my credit history. I live with my brother and have no car payment or credit cards. I am in community college and work full time. Im going to use the money to make some repairs around the house and get some maintenance done on my car.<br><br> Borrower added on 12/20/11 > $1000 down only $4375 to go. Thanks to everyone that invested so far, looking forward to surprising my brother with the fixes around the house.<br>otherBuilding my credit history.774xxTX18.08NaNNaNfNaN0.0NaN1NaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNNaNNaN
1010700781305201650065006500.060 months14.65%153.45CC3Southwest Rural metro5 yearsOWN72000.0Not VerifiedDec-11Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=1070078Borrower added on 12/15/11 > I had recived a loan from Citi Financial about a year ago, I was paying 29.99 intrest, so the refinance is to cut that rate since cleaning up my credit I have been paying everything on time as shown on my credit report<br>debt_consolidationHigh intrest Consolidation853xxAZ16.12NaNNaNfNaN0.0NaN1NaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNNaNNaN
idmember_idloan_amntfunded_amntfunded_amnt_invtermint_rateinstallmentgradesub_gradeemp_titleemp_lengthhome_ownershipannual_incverification_statusissue_dloan_statuspymnt_planurldescpurposetitlezip_codeaddr_statedtimths_since_last_delinqmths_since_last_recordinitial_list_statusnext_pymnt_dcollections_12_mths_ex_medmths_since_last_major_derogpolicy_codeannual_inc_jointdti_jointverification_status_jointacc_now_delinqtot_coll_amttot_cur_balopen_acc_6mopen_il_6mopen_il_12mopen_il_24mmths_since_rcnt_iltotal_bal_ilil_utilopen_rv_12mopen_rv_24mmax_bal_bcall_utiltotal_rev_hi_liminq_fitotal_cu_tlinq_last_12macc_open_past_24mthsavg_cur_balbc_open_to_buybc_utilchargeoff_within_12_mthsdelinq_amntmo_sin_old_il_acctmo_sin_old_rev_tl_opmo_sin_rcnt_rev_tl_opmo_sin_rcnt_tlmort_accmths_since_recent_bcmths_since_recent_bc_dlqmths_since_recent_inqmths_since_recent_revol_delinqnum_accts_ever_120_pdnum_actv_bc_tlnum_actv_rev_tlnum_bc_satsnum_bc_tlnum_il_tlnum_op_rev_tlnum_rev_acctsnum_rev_tl_bal_gt_0num_satsnum_tl_120dpd_2mnum_tl_30dpdnum_tl_90g_dpd_24mnum_tl_op_past_12mpct_tl_nvr_dlqpercent_bc_gt_75pub_rec_bankruptciestax_lienstot_hi_cred_limtotal_bal_ex_morttotal_bc_limittotal_il_high_credit_limit
39707926669266150005000525.036 months9.33%159.77BB3Stark and Roth Inc2 yearsMORTGAGE180000.0Not VerifiedJul-07Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=92666Need a loan to make some home improvmentshome_improvementhome improvment loan530xxWI11.930.00.0fNaNNaNNaN1NaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
39708925529254250005000375.036 months9.96%161.25BB5Millenium Group4 yearsMORTGAGE48000.0Not VerifiedJul-07Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=92552I would like to pay off my high-interest credit card debts and have a single payment to make every monthdebt_consolidationTito5000333xxFL8.030.00.0fNaNNaNNaN1NaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
39709925339252950005000675.036 months11.22%164.23CC4Self-Employeed< 1 yearOWN80000.0Not VerifiedJul-07Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=92533NaNcredit_cardP's Family Credit Loan537xxWI1.210.044.0fNaNNaNNaN1NaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
39710925079250250005000250.036 months7.43%155.38AA2Rush Univ Med Grp1 yearOWN85000.0Not VerifiedJul-07Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=92507NaNcredit_cardMy Credit Card Loan537xxWI0.310.00.0fNaNNaNNaN1NaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
39711924029239050005000700.036 months8.70%158.30BB1A. F. Wolfers, Inc.5 yearsMORTGAGE75000.0Not VerifiedJul-07Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=92402I'd like to shift some credit card debt so it has a lower interest rate.credit_cardReduce Credit Card Debt804xxCO15.550.00.0fNaNNaNNaN1NaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
397129218792174250025001075.036 months8.07%78.42AA4FiSite Research4 yearsMORTGAGE110000.0Not VerifiedJul-07Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=92187Our current gutter system on our home is old and in need of repair. We will be using the borrowed funds to replace the gutter system on our home.home_improvementHome Improvement802xxCO11.330.00.0fNaNNaNNaN1NaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
39713906659060785008500875.036 months10.28%275.38CC1Squarewave Solutions, Ltd.3 yearsRENT18000.0Not VerifiedJul-07Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=90665The rate of interest and fees incurred by carrying a balance on my credit card are so outrageous at this point that continuing to pay them is patently bad financial thinking. I wish to redirect my efforts at retiring my debt via another more-reasonable means. I have sufficient funds to direct to this end on a monthly basis, and have simply gotten tired of their being gobbled up by interest and fees.credit_cardRetiring credit card debt274xxNC6.405.00.0fNaNNaNNaN1NaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
397149039590390500050001325.036 months8.07%156.84AA4NaN< 1 yearMORTGAGE100000.0Not VerifiedJul-07Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=90395NaNdebt_consolidationMBA Loan Consolidation017xxMA2.300.00.0fNaNNaNNaN1NaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
39715903768924350005000650.036 months7.43%155.38AA2NaN< 1 yearMORTGAGE200000.0Not VerifiedJul-07Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=90376NaNotherJAL Loan208xxMD3.720.00.0fNaNNaNNaN1NaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
39716870238699975007500800.036 months13.75%255.43EE2Evergreen Center< 1 yearOWN22000.0Not VerifiedJun-07Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=87023I plan to consolidate over $7,000 of debt: a combination of credit cards and student loans.debt_consolidationConsolidation Loan027xxMA14.2911.00.0fNaNNaNNaN1NaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN